LOCAL PROBLEM = problem08
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.2D-02
 Smallest constraints scale factor : 1.3D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         2.180D+01 0.D+00  4.812D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.749D+01 4.D-01 -1.931D+00 5.D-02 5.D-02 7.D-08 4.D-02    5C  0   0
  2 2.D+01 -8.649D+01 2.D-02 -1.909D+00 3.D-03 3.D-03 8.D-06 3.D-03    7C  0   0
  3 2.D+01 -8.643D+01 1.D-03 -1.908D+00 2.D-04 2.D-04 6.D-05 2.D-04    8C  0   0
  4 2.D+01 -8.642D+01 1.D-04 -1.908D+00 1.D-05 1.D-05 2.D-07 1.D-05    9C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  1.1134D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.0122D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.5440D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.2747D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  9.8419D-11
 Sup-norm of the gradient of the Lagrangian                     =  1.4030D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.2749D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.2790D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     23
 Subroutine evalg    (coded=T):     28
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     51 (    25 calls per constraint in average)
 Subroutine evaljac  (coded=T):     28 (    14 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     11
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 5.3D-02
 Smallest constraints scale factor : 1.6D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -8.107D+01 2.D+00 -4.282D+00 3.D-01 3.D-01 5.D-01 3.D-01    0   0   0
  1 4.D+01 -8.686D+01 1.D-01 -4.588D+00 2.D-02 2.D-02 5.D-07 3.D-02    4C  0   0
  2 5.D+01 -8.644D+01 6.D-03 -4.566D+00 9.D-04 9.D-04 7.D-07 1.D-03    6C  0   0
  3 5.D+01 -8.642D+01 3.D-04 -4.565D+00 4.D-05 4.D-05 2.D-05 4.D-05    7C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  2.5167D-04
 Sup-norm of the gradient of the Lagrangian                     =  3.1933D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.4900D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.8973D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  5.0289D-10
 Sup-norm of the gradient of the Lagrangian                     =  3.1715D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.8819D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.9132D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     19
 Subroutine evalg    (coded=T):     23
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     43 (    21 calls per constraint in average)
 Subroutine evaljac  (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      9
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 6.0D-04
 Smallest constraints scale factor : 5.7D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         2.940D+03 0.D+00  1.764D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 2.D+01 -8.652D+01 3.D-02 -5.190D-02 2.D-03 2.D-03 1.D-05 7.D-04    7C  0   0
  2 1.D+01 -8.642D+01 5.D-04 -5.185D-02 3.D-05 3.D-05 1.D-05 1.D-05    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6424D+01
 Maximal violation of selected nearly-active constraints        =  5.0295D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.1847D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  6.9749D-05
 Sup-norm of the gradient of the Lagrangian                     =  3.3452D-07

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  2.0086D-09
 Sup-norm of the gradient of the Lagrangian                     =  6.3386D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  5.7597D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  5.8975D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     31
 Subroutine evalg    (coded=T):     22
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     67 (    33 calls per constraint in average)
 Subroutine evaljac  (coded=T):     17 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.0D-03
 Smallest constraints scale factor : 6.6D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.413D+03 0.D+00  1.451D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.657D+01 5.D-02 -8.888D-02 3.D-03 3.D-03 4.D-05 2.D-03    4C  0   0
  2 1.D+01 -8.643D+01 1.D-03 -8.874D-02 7.D-05 7.D-05 4.D-05 3.D-05    5C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6425D+01
 Maximal violation of selected nearly-active constraints        =  1.0037D-03
 Sup-norm of the gradient of the Lagrangian                     =  4.0798D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.3919D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.2242D-06

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  8.0001D-09
 Sup-norm of the gradient of the Lagrangian                     =  1.2651D-12

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.1495D-12

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.1724D-12

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     28
 Subroutine evalg    (coded=T):     19
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     61 (    30 calls per constraint in average)
 Subroutine evaljac  (coded=T):     16 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      7
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.9D-03
 Smallest constraints scale factor : 1.1D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         3.919D+02 4.D-01  7.611D-01 4.D-01 4.D-01 1.D+00 4.D-01    0   0   0
  1 1.D+01 -1.170D+02 0.D+00 -2.272D-01 0.D+00 0.D+00 5.D-08 0.D+00    5C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  2.4617D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  1.2308D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.4106D-12

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  3.7748D-13
 Sup-norm of the gradient of the Lagrangian                     =  4.6130D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  3.0771D-16

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  4.6438D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     19
 Subroutine evalg    (coded=T):     16
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     43 (    21 calls per constraint in average)
 Subroutine evaljac  (coded=T):     14 (     7 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      7
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.170000000000000e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.0D-03
 Smallest constraints scale factor : 6.6D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.359D+03 0.D+00  1.407D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.657D+01 5.D-02 -8.960D-02 3.D-03 3.D-03 3.D-09 2.D-03    5C  0   0
  2 1.D+01 -8.642D+01 9.D-04 -8.945D-02 6.D-05 6.D-05 5.D-05 3.D-05    6C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6425D+01
 Maximal violation of selected nearly-active constraints        =  9.1156D-04
 Sup-norm of the gradient of the Lagrangian                     =  4.3912D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.2642D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.2502D-06

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  6.5988D-09
 Sup-norm of the gradient of the Lagrangian                     =  1.1489D-12

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.0440D-12

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.0445D-12

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     24
 Subroutine evalg    (coded=T):     19
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     51 (    25 calls per constraint in average)
 Subroutine evaljac  (coded=T):     17 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      8
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 4.7D-03
 Smallest constraints scale factor : 9.5D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         8.925D+01 0.D+00  4.222D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.687D+01 2.D-01 -4.109D-01 1.D-02 1.D-02 8.D-05 1.D-02    4C  0   0
  2 1.D+01 -8.644D+01 7.D-03 -4.089D-01 7.D-04 7.D-04 6.D-08 5.D-04    6C  0   0
  3 1.D+01 -8.642D+01 3.D-04 -4.088D-01 3.D-05 3.D-05 2.D-06 2.D-05    7C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  3.1000D-04
 Sup-norm of the gradient of the Lagrangian                     =  3.7227D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.2990D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.9038D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  7.6303D-10
 Sup-norm of the gradient of the Lagrangian                     =  3.9066D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  3.5499D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  3.6593D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     30
 Subroutine evalg    (coded=T):     23
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     64 (    32 calls per constraint in average)
 Subroutine evaljac  (coded=T):     21 (    10 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      9
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.7D-03
 Smallest constraints scale factor : 1.2D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.260D+02 6.D+00  3.348D-01 6.D+00 6.D+00 1.D+00 6.D+00    0   0   0
  1 5.D-01 -1.346D+02 2.D+00 -3.577D-01 3.D-01 3.D-01 1.D-08 1.D-01    5C  0   0
  2 1.D+01 -1.170D+02 0.D+00 -3.109D-01 0.D+00 0.D+00 1.D-07 0.D+00   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  3.6569D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  1.8284D-06
 Sup-norm of the gradient of the Lagrangian                     =  7.5318D-12

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  8.3489D-13
 Sup-norm of the gradient of the Lagrangian                     =  3.4029D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  4.5711D-16

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  3.4486D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     30
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     67 (    33 calls per constraint in average)
 Subroutine evaljac  (coded=T):     29 (    14 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     12
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.170000000000000e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.9D-03
 Smallest constraints scale factor : 7.7D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         5.986D+02 0.D+00  1.126D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.667D+01 8.D-02 -1.631D-01 6.D-03 6.D-03 6.D-07 4.D-03    5C  0   0
  2 1.D+01 -8.643D+01 2.D-03 -1.626D-01 2.D-04 2.D-04 4.D-09 1.D-04    7C  0   0
  3 1.D+01 -8.642D+01 6.D-05 -1.626D-01 5.D-06 5.D-06 1.D-07 3.D-06    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  6.4928D-05
 Sup-norm of the gradient of the Lagrangian                     =  7.0018D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  9.0039D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.2936D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.3470D-11
 Sup-norm of the gradient of the Lagrangian                     =  8.1818D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  7.4346D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  8.5265D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     32
 Subroutine evalg    (coded=T):     25
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     70 (    35 calls per constraint in average)
 Subroutine evaljac  (coded=T):     21 (    10 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 4.1D-03
 Smallest constraints scale factor : 9.3D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.347D+02 0.D+00  5.563D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.684D+01 1.D-01 -3.586D-01 1.D-02 1.D-02 2.D-06 9.D-03    5C  0   0
  2 1.D+01 -8.644D+01 6.D-03 -3.570D-01 5.D-04 5.D-04 4.D-08 4.D-04    7C  0   0
  3 1.D+01 -8.642D+01 2.D-04 -3.569D-01 2.D-05 2.D-05 1.D-06 2.D-05    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  2.4495D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.9083D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.3969D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.8162D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.7640D-10
 Sup-norm of the gradient of the Lagrangian                     =  3.0868D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.8049D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  3.0198D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     32
 Subroutine evalg    (coded=T):     25
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     70 (    35 calls per constraint in average)
 Subroutine evaljac  (coded=T):     21 (    10 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 5.7D-02
 Smallest constraints scale factor : 2.7D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -6.536D+01 8.D+00 -3.743D+00 2.D+00 2.D+00 1.D+00 2.D+00    0   0   0
  1 2.D+01 -8.687D+01 1.D-01 -4.975D+00 4.D-02 4.D-02 5.D-08 8.D-02    4C  0   0
  2 5.D+01 -8.643D+01 2.D-03 -4.950D+00 6.D-04 6.D-04 3.D-06 1.D-03    6C  0   0
  3 5.D+01 -8.642D+01 3.D-05 -4.950D+00 8.D-06 8.D-06 7.D-06 2.D-05    7C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.0990D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.1942D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.2976D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.9219D-10

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  7.6251D-12
 Sup-norm of the gradient of the Lagrangian                     =  3.9052D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  3.5485D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  4.2633D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     22
 Subroutine evalg    (coded=T):     23
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     49 (    24 calls per constraint in average)
 Subroutine evaljac  (coded=T):     25 (    12 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      9
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 4.7D-02
 Smallest constraints scale factor : 4.5D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.748D+01 6.D+00 -8.264D-01 3.D+00 3.D+00 1.D+00 3.D+00    0   0   0
  1 3.D+00  8.000D+00 0.D+00  3.783D-01 0.D+00 0.D+00 9.D-05 0.D+00    3C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  2.0036D-03

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  6.0095D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.4077D-07

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  1.5050D-08
 Sup-norm of the gradient of the Lagrangian                     =  1.7348D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  7.5383D-17

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     12
 Subroutine evalg    (coded=T):     13
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     25 (    12 calls per constraint in average)
 Subroutine evaljac  (coded=T):     13 (     6 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      6
 Subroutine evalhlp  (coded=F):      0

LOCAL f = 8.000000000000000e+00
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 4.9D-04
 Smallest constraints scale factor : 5.4D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         3.926D+03 0.D+00  1.907D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 2.D+01 -8.650D+01 3.D-02 -4.202D-02 1.D-03 1.D-03 3.D-05 5.D-04    6C  0   0
  2 1.D+01 -8.642D+01 5.D-04 -4.198D-02 3.D-05 3.D-05 8.D-06 1.D-05    7C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6424D+01
 Maximal violation of selected nearly-active constraints        =  4.7427D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.6905D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  6.5770D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.3002D-07

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.7860D-09
 Sup-norm of the gradient of the Lagrangian                     =  5.9770D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  5.4312D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  5.5067D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     32
 Subroutine evalg    (coded=T):     22
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     71 (    35 calls per constraint in average)
 Subroutine evaljac  (coded=T):     16 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      9
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.5D-03
 Smallest constraints scale factor : 9.9D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         6.230D+02 5.D+00  9.111D-01 5.D+00 5.D+00 1.D+00 5.D+00    0   0   0
  1 9.D-01 -1.280D+02 1.D+00 -1.872D-01 1.D-01 1.D-01 8.D-06 7.D-02    4C  0   0
  2 1.D+01 -1.184D+02 0.D+00 -1.732D-01 0.D+00 9.D-03 5.D-05 0.D+00   10C  0   0
  3 1.D+01 -1.187D+02 5.D-03 -1.736D-01 5.D-04 5.D-04 2.D-06 2.D-04   13C  0   0
  4 1.D+01 -1.187D+02 0.D+00 -1.736D-01 0.D+00 3.D-05 6.D-07 0.D+00   14C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  8.1051D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-07

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-06

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-05

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-04

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-03

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-01

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+00

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+01

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.1523D-05
 Sup-norm of the gradient of the Lagrangian                     =  8.1051D-02

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.8156D-05
 Sup-norm of the gradient of the Lagrangian                     =  6.0264D-02

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.0042D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.4815D-02

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.5535D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.3327D-02

 NEWTON-KKT Iteration                                           =           5
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.0712D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.4784D-02

 NEWTON-KKT Iteration                                           =           6
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.6984D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.8430D-02

 NEWTON-KKT Iteration                                           =           7
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  9.3917D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.3705D-02

 NEWTON-KKT Iteration                                           =           8
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.1933D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.0192D-02

 NEWTON-KKT Iteration                                           =           9
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.8716D-07
 Sup-norm of the gradient of the Lagrangian                     =  7.5788D-03

 NEWTON-KKT Iteration                                           =          10
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.5877D-07
 Sup-norm of the gradient of the Lagrangian                     =  5.6357D-03

 Flag of NEWTON-KKT = Maximum of iterations reached.
  5 1.D+01 -1.187D+02 2.D-05 -1.736D-01 2.D-06 2.D-06 2.D-09 8.D-07   15C  1  10

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.8224D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.1790D-06

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.5539D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.7103D-11

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.0180D-10
 Sup-norm of the gradient of the Lagrangian                     =  1.4269D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  5.7287D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  7.9936D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     51
 Subroutine evalg    (coded=T):     50
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):    110 (    55 calls per constraint in average)
 Subroutine evaljac  (coded=T):     64 (    32 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     27
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 5.6D-02
 Smallest constraints scale factor : 4.1D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -5.215D+01 9.D+00 -2.938D+00 4.D+00 4.D+00 1.D+00 4.D+00    0   0   0
  1 4.D+00  8.000D+00 0.D+00  4.507D-01 0.D+00 0.D+00 9.D-06 0.D+00    6C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.5724D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  4.7172D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.4835D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  9.2717D-11
 Sup-norm of the gradient of the Lagrangian                     =  1.3617D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     14
 Subroutine evalg    (coded=T):     15
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     29 (    14 calls per constraint in average)
 Subroutine evaljac  (coded=T):     11 (     5 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      8
 Subroutine evalhlp  (coded=F):      0

LOCAL f = 8.000000000000000e+00
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 4.8D-03
 Smallest constraints scale factor : 9.5D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.566D+02 0.D+00  7.456D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.687D+01 2.D-01 -4.136D-01 1.D-02 1.D-02 2.D-09 1.D-02    5C  0   0
  2 1.D+01 -8.644D+01 7.D-03 -4.116D-01 7.D-04 7.D-04 6.D-08 4.D-04    7C  0   0
  3 1.D+01 -8.642D+01 3.D-04 -4.115D-01 3.D-05 3.D-05 2.D-06 2.D-05    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  3.0834D-04
 Sup-norm of the gradient of the Lagrangian                     =  3.6417D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.2760D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.8801D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  7.5489D-10
 Sup-norm of the gradient of the Lagrangian                     =  3.8857D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  3.5309D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  3.5882D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     27
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     58 (    29 calls per constraint in average)
 Subroutine evaljac  (coded=T):     22 (    11 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.2D-02
 Smallest constraints scale factor : 2.0D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.154D+02 0.D+00 -2.553D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 3.D+01 -1.190D+02 8.D-02 -2.633D+00 2.D-02 2.D-02 5.D-05 1.D-02    4C  0   0
  2 3.D+01 -1.187D+02 0.D+00 -2.626D+00 0.D+00 1.D-03 5.D-05 0.D+00    6C  0   0
  3 3.D+01 -1.187D+02 5.D-04 -2.627D+00 1.D-04 1.D-04 9.D-06 9.D-05    7C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  4.9088D-04
 Sup-norm of the gradient of the Lagrangian                     =  4.2272D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  8.8443D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.2245D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.3060D-08
 Sup-norm of the gradient of the Lagrangian                     =  3.5518D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.3060D-08
 Sup-norm of the gradient of the Lagrangian                     =  3.5518D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  5.4307D-15

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     21
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     44 (    22 calls per constraint in average)
 Subroutine evaljac  (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     11
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 3.3D-03
 Smallest constraints scale factor : 1.3D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.009D+02 9.D-01  3.310D-01 9.D-01 9.D-01 1.D+00 9.D-01    0   0   0
  1 1.D+01 -1.190D+02 7.D-02 -3.902D-01 9.D-03 9.D-03 4.D-05 5.D-03    7C  0   0
  2 1.D+01 -1.187D+02 0.D+00 -3.893D-01 0.D+00 7.D-04 6.D-06 0.D+00    9C  0   0
  3 1.D+01 -1.187D+02 4.D-04 -3.894D-01 5.D-05 5.D-05 1.D-06 3.D-05   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  4.2678D-04
 Sup-norm of the gradient of the Lagrangian                     =  3.5263D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.7182D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.6565D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.8022D-08
 Sup-norm of the gradient of the Lagrangian                     =  3.0995D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.8022D-08
 Sup-norm of the gradient of the Lagrangian                     =  3.0995D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.9278D-16

 Maximal violation of constraints                               =  4.4409D-16

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     32
 Subroutine evalg    (coded=T):     29
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     71 (    35 calls per constraint in average)
 Subroutine evaljac  (coded=T):     34 (    17 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     14
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 3.4D-04
 Smallest constraints scale factor : 4.9D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         6.485D+03 0.D+00  2.176D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 2.D+01 -8.648D+01 2.D-02 -2.902D-02 1.D-03 1.D-03 1.D-06 3.D-04    7C  0   0
  2 1.D+01 -8.642D+01 2.D-04 -2.900D-02 1.D-05 1.D-05 4.D-06 4.D-06    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  2.3648D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.1251D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.2794D-05
 Sup-norm of the gradient of the Lagrangian                     =  8.2850D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.4402D-10
 Sup-norm of the gradient of the Lagrangian                     =  2.9801D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.7079D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.8422D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     34
 Subroutine evalg    (coded=T):     23
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     75 (    37 calls per constraint in average)
 Subroutine evaljac  (coded=T):     16 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 4.7D-02
 Smallest constraints scale factor : 4.6D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -8.637D+01 1.D+01 -4.044D+00 5.D+00 5.D+00 1.D+00 6.D+00    0   0   0
  1 3.D+00 -8.706D+01 2.D-01 -4.076D+00 1.D-01 1.D-01 6.D-05 3.D-01    4C  0   0
  2 4.D+01 -8.643D+01 1.D-03 -4.046D+00 5.D-04 5.D-04 3.D-05 2.D-03    6C  0   0
  3 4.D+01 -8.642D+01 6.D-06 -4.046D+00 3.D-06 3.D-06 4.D-06 9.D-06    7C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  5.8173D-06
 Sup-norm of the gradient of the Lagrangian                     =  8.8909D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  8.0672D-07
 Sup-norm of the gradient of the Lagrangian                     =  8.0931D-12

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  2.6845D-13
 Sup-norm of the gradient of the Lagrangian                     =  2.1761D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  6.6610D-15

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.8422D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     21
 Subroutine evalg    (coded=T):     23
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     48 (    24 calls per constraint in average)
 Subroutine evaljac  (coded=T):     30 (    15 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      9
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 7.8D-02
 Smallest constraints scale factor : 2.7D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.213D+02 2.D+00 -9.478D+00 5.D-01 5.D-01 1.D+00 5.D-01    0   0   0
  1 9.D+01 -1.188D+02 4.D-02 -9.284D+00 1.D-02 1.D-02 4.D-07 1.D-02    6C  0   0
  2 9.D+01 -1.187D+02 0.D+00 -9.273D+00 0.D+00 5.D-04 5.D-05 0.D+00    8C  0   0
  3 9.D+01 -1.187D+02 7.D-05 -9.273D+00 2.D-05 2.D-05 4.D-06 2.D-05    9C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  7.0532D-05
 Sup-norm of the gradient of the Lagrangian                     =  5.4286D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2848D-04
 Sup-norm of the gradient of the Lagrangian                     =  4.4262D-10

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.3305D-09
 Sup-norm of the gradient of the Lagrangian                     =  5.1586D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.0711D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.5047D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     23
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     49 (    24 calls per constraint in average)
 Subroutine evaljac  (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     11
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.2D-01
 Smallest constraints scale factor : 2.4D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -7.810D+00 3.D+00 -9.013D-01 7.D-01 7.D-01 1.D+00 7.D-01    0   0   0
  1 1.D+01 -8.784D+01 5.D-01 -1.014D+01 1.D-01 1.D-01 1.D-05 2.D-01    6C  0   0
  2 1.D+02 -8.645D+01 9.D-03 -9.977D+00 2.D-03 2.D-03 5.D-10 4.D-03    9C  0   0
  3 1.D+02 -8.642D+01 2.D-04 -9.974D+00 4.D-05 4.D-05 5.D-11 6.D-05   11C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  1.5298D-04
 Sup-norm of the gradient of the Lagrangian                     =  4.0562D-10

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  2.1214D-05
 Sup-norm of the gradient of the Lagrangian                     =  8.1523D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.8581D-10
 Sup-norm of the gradient of the Lagrangian                     =  1.9278D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.7517D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.8474D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     25
 Subroutine evalg    (coded=T):     27
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     55 (    27 calls per constraint in average)
 Subroutine evaljac  (coded=T):     29 (    14 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     13
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.9D-02
 Smallest constraints scale factor : 9.4D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -6.810D+01 5.D+00 -1.297D+00 4.D+00 4.D+00 1.D+00 4.D+00    0   0   0
  1 1.D+00 -1.170D+02 0.D+00 -2.228D+00 0.D+00 0.D+00 1.D-04 0.D+00    4C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  5.0637D-03

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  2.5317D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.4421D-07

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  1.6021D-08
 Sup-norm of the gradient of the Lagrangian                     =  1.7898D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  3.2026D-16

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     13
 Subroutine evalg    (coded=T):     14
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evaljac  (coded=T):     13 (     6 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      7
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.170000000000000e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 9.4D-02
 Smallest constraints scale factor : 1.6D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -2.016D+01 0.D+00 -1.892D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 2.D+01 -8.783D+01 5.D-01 -8.239D+00 8.D-02 8.D-02 5.D-07 9.D-02    6C  0   0
  2 8.D+01 -8.648D+01 2.D-02 -8.112D+00 3.D-03 3.D-03 2.D-10 4.D-03    9C  0   0
  3 8.D+01 -8.642D+01 8.D-04 -8.107D+00 1.D-04 1.D-04 4.D-10 1.D-04   11C  0   0
  4 8.D+01 -8.642D+01 3.D-05 -8.107D+00 5.D-06 5.D-06 5.D-07 6.D-06   12C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.0637D-05
 Sup-norm of the gradient of the Lagrangian                     =  5.4503D-06

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.2486D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.2050D-10

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  7.4519D-12
 Sup-norm of the gradient of the Lagrangian                     =  3.8607D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  3.5081D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  4.9738D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     28
 Subroutine evalg    (coded=T):     30
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     59 (    29 calls per constraint in average)
 Subroutine evaljac  (coded=T):     31 (    15 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     14
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.6D-03
 Smallest constraints scale factor : 1.0D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         5.569D+02 2.D+00  8.873D-01 2.D+00 2.D+00 1.D+00 2.D+00    0   0   0
  1 4.D+00 -1.192D+02 1.D-01 -1.900D-01 1.D-02 1.D-02 3.D-05 7.D-03    9C  0   0
  2 1.D+01 -1.187D+02 0.D+00 -1.891D-01 0.D+00 8.D-04 2.D-09 0.D+00   12C  0   0
  3 1.D+01 -1.187D+02 5.D-04 -1.891D-01 5.D-05 5.D-05 2.D-06 2.D-05   13C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  4.6248D-04
 Sup-norm of the gradient of the Lagrangian                     =  9.7811D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.2663D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.5802D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.2564D-08
 Sup-norm of the gradient of the Lagrangian                     =  2.9180D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.2564D-08
 Sup-norm of the gradient of the Lagrangian                     =  2.9180D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  8.8132D-15

 Maximal violation of constraints                               =  1.7764D-15

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     34
 Subroutine evalg    (coded=T):     32
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     75 (    37 calls per constraint in average)
 Subroutine evaljac  (coded=T):     39 (    19 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     17
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.5D-02
 Smallest constraints scale factor : 1.2D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         3.909D+01 0.D+00  5.853D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.727D+01 3.D-01 -1.307D+00 3.D-02 3.D-02 3.D-07 3.D-02    4C  0   0
  2 1.D+01 -8.648D+01 2.D-02 -1.295D+00 2.D-03 2.D-03 2.D-06 2.D-03    6C  0   0
  3 1.D+01 -8.643D+01 1.D-03 -1.294D+00 2.D-04 2.D-04 3.D-05 1.D-04    7C  0   0
  4 1.D+01 -8.642D+01 1.D-04 -1.294D+00 1.D-05 1.D-05 1.D-07 1.D-05    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  1.1523D-04
 Sup-norm of the gradient of the Lagrangian                     =  8.9302D-06

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.5979D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.5856D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.0542D-10
 Sup-norm of the gradient of the Lagrangian                     =  1.4520D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.3194D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.5277D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     22
 Subroutine evalg    (coded=T):     27
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     49 (    24 calls per constraint in average)
 Subroutine evaljac  (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.0D-03
 Smallest constraints scale factor : 7.8D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         4.900D+02 0.D+00  9.799D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.671D+01 1.D-01 -1.734D-01 7.D-03 7.D-03 5.D-05 4.D-03    5C  0   0
  2 1.D+01 -8.643D+01 3.D-03 -1.728D-01 2.D-04 2.D-04 7.D-09 1.D-04    7C  0   0
  3 1.D+01 -8.642D+01 8.D-05 -1.728D-01 6.D-06 6.D-06 2.D-07 4.D-06    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  8.2532D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.0339D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.1445D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.0446D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  5.4080D-11
 Sup-norm of the gradient of the Lagrangian                     =  1.0400D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  9.4504D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.1013D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     33
 Subroutine evalg    (coded=T):     25
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     72 (    36 calls per constraint in average)
 Subroutine evaljac  (coded=T):     21 (    10 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 6.9D-03
 Smallest constraints scale factor : 1.6D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -5.330D+01 0.D+00 -3.674D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -1.191D+02 1.D-01 -8.209D-01 2.D-02 2.D-02 4.D-06 1.D-02    6C  0   0
  2 1.D+01 -1.187D+02 0.D+00 -8.180D-01 0.D+00 2.D-03 2.D-05 0.D+00    8C  0   0
  3 1.D+01 -1.187D+02 1.D-03 -8.183D-01 2.D-04 2.D-04 7.D-06 1.D-04    9C  0   0
  4 1.D+01 -1.187D+02 0.D+00 -8.183D-01 0.D+00 2.D-05 8.D-08 0.D+00   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  5.2671D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-07

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-06

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-05

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-04

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-03

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-01

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+00

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+01

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.9460D-07
 Sup-norm of the gradient of the Lagrangian                     =  5.2671D-02

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.6682D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.9163D-02

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.2401D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.9121D-02

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.3445D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.1654D-02

 NEWTON-KKT Iteration                                           =           5
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2963D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.6101D-02

 NEWTON-KKT Iteration                                           =           6
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.1674D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.1973D-02

 NEWTON-KKT Iteration                                           =           7
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.9629D-07
 Sup-norm of the gradient of the Lagrangian                     =  8.9028D-03

 NEWTON-KKT Iteration                                           =           8
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.1911D-07
 Sup-norm of the gradient of the Lagrangian                     =  6.6199D-03

 NEWTON-KKT Iteration                                           =           9
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2114D-07
 Sup-norm of the gradient of the Lagrangian                     =  4.9224D-03

 NEWTON-KKT Iteration                                           =          10
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.6979D-08
 Sup-norm of the gradient of the Lagrangian                     =  3.6601D-03

 Flag of NEWTON-KKT = Maximum of iterations reached.
  5 1.D+01 -1.187D+02 1.D-05 -8.183D-01 2.D-06 2.D-06 8.D-10 1.D-06   11C  1  10

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2736D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.1380D-07

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.5007D-05
 Sup-norm of the gradient of the Lagrangian                     =  7.7627D-12

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.0401D-11
 Sup-norm of the gradient of the Lagrangian                     =  1.0040D-13

 Maximal violation of constraints                               =  1.7764D-15

 Maximal violation of Lagrange multipliers non-negativity       =  4.0309D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  4.0412D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     40
 Subroutine evalg    (coded=T):     44
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     84 (    42 calls per constraint in average)
 Subroutine evaljac  (coded=T):     55 (    27 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     23
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 7.2D-04
 Smallest constraints scale factor : 7.8D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         2.049D+03 2.D+00  1.473D+00 2.D+00 2.D+00 1.D+00 2.D+00    0   0   0
  1 6.D+00 -1.190D+02 7.D-02 -8.553D-02 5.D-03 5.D-03 2.D-05 2.D-03    7C  0   0
  2 1.D+01 -1.187D+02 0.D+00 -8.534D-02 0.D+00 3.D-04 6.D-05 0.D+00    8C  0   0
  3 1.D+01 -1.187D+02 9.D-04 -8.535D-02 7.D-05 7.D-05 5.D-06 2.D-05    9C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  9.1781D-04
 Sup-norm of the gradient of the Lagrangian                     =  6.3307D-03

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.4578D-03
 Sup-norm of the gradient of the Lagrangian                     =  2.7916D-07

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7137D-07
 Sup-norm of the gradient of the Lagrangian                     =  5.8558D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7137D-07
 Sup-norm of the gradient of the Lagrangian                     =  5.8558D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.2204D-15
 Sup-norm of the gradient of the Lagrangian                     =  8.6056D-15

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     33
 Subroutine evalg    (coded=T):     28
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     73 (    36 calls per constraint in average)
 Subroutine evaljac  (coded=T):     33 (    16 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     13
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.3D-03
 Smallest constraints scale factor : 7.0D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.002D+03 0.D+00  1.264D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.660D+01 6.D-02 -1.092D-01 4.D-03 4.D-03 2.D-05 2.D-03    5C  0   0
  2 1.D+01 -8.643D+01 1.D-03 -1.090D-01 9.D-05 9.D-05 7.D-05 4.D-05    6C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6426D+01
 Maximal violation of selected nearly-active constraints        =  1.2580D-03
 Sup-norm of the gradient of the Lagrangian                     =  5.9231D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.7445D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.3148D-06

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.2568D-08
 Sup-norm of the gradient of the Lagrangian                     =  1.5857D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.2568D-08
 Sup-norm of the gradient of the Lagrangian                     =  1.5857D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.1418D-16

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     30
 Subroutine evalg    (coded=T):     21
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     63 (    31 calls per constraint in average)
 Subroutine evaljac  (coded=T):     20 (    10 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.4D-02
 Smallest constraints scale factor : 1.2D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         3.901D+01 0.D+00  5.643D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.725D+01 3.D-01 -1.262D+00 3.D-02 3.D-02 5.D-07 3.D-02    4C  0   0
  2 1.D+01 -8.648D+01 2.D-02 -1.251D+00 2.D-03 2.D-03 2.D-06 2.D-03    6C  0   0
  3 1.D+01 -8.643D+01 1.D-03 -1.250D+00 2.D-04 2.D-04 3.D-05 1.D-04    7C  0   0
  4 1.D+01 -8.642D+01 1.D-04 -1.250D+00 1.D-05 1.D-05 1.D-07 1.D-05    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  1.1592D-04
 Sup-norm of the gradient of the Lagrangian                     =  8.8489D-06

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.6075D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.6414D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.0669D-10
 Sup-norm of the gradient of the Lagrangian                     =  1.4608D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.3274D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.5632D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     22
 Subroutine evalg    (coded=T):     27
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     49 (    24 calls per constraint in average)
 Subroutine evaljac  (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.3D-02
 Smallest constraints scale factor : 1.0D+00

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -6.410D+01 9.D+00 -1.500D+00 9.D+00 9.D+00 1.D+00 6.D+00    0   0   0
  1 4.D-01  8.000D+00 0.D+00  1.872D-01 0.D+00 0.D+00 2.D-06 0.D+00    7C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.0409D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  3.1227D-05
 Sup-norm of the gradient of the Lagrangian                     =  6.5015D-10

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  4.0631D-11
 Sup-norm of the gradient of the Lagrangian                     =  9.0145D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     15
 Subroutine evalg    (coded=T):     16
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     31 (    15 calls per constraint in average)
 Subroutine evaljac  (coded=T):     11 (     5 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      9
 Subroutine evalhlp  (coded=F):      0

LOCAL f = 8.000000000000000e+00
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 7.2D-04
 Smallest constraints scale factor : 7.8D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.997D+03 6.D+00  1.442D+00 6.D+00 6.D+00 1.D+00 6.D+00    0   0   0
  1 7.D-01 -1.279D+02 1.D+00 -9.230D-02 1.D-01 1.D-01 4.D-05 4.D-02    6C  0   0
  2 1.D+01 -1.185D+02 0.D+00 -8.551D-02 0.D+00 5.D-03 6.D-05 0.D+00   12C  0   0
  3 1.D+01 -1.187D+02 3.D-03 -8.568D-02 2.D-04 2.D-04 6.D-05 7.D-05   14C  0   0
  4 1.D+01 -1.187D+02 5.D-04 -8.568D-02 4.D-05 4.D-05 9.D-07 1.D-05   15C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  5.3384D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.3035D-03

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.1134D-03
 Sup-norm of the gradient of the Lagrangian                     =  4.7794D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  9.9953D-08
 Sup-norm of the gradient of the Lagrangian                     =  4.4719D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  9.9953D-08
 Sup-norm of the gradient of the Lagrangian                     =  4.4719D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  7.2665D-15

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     38
 Subroutine evalg    (coded=T):     36
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     81 (    40 calls per constraint in average)
 Subroutine evaljac  (coded=T):     42 (    21 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     19
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.3D-03
 Smallest constraints scale factor : 7.0D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.021D+03 0.D+00  1.293D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.660D+01 6.D-02 -1.097D-01 4.D-03 4.D-03 4.D-05 2.D-03    5C  0   0
  2 1.D+01 -8.643D+01 1.D-03 -1.094D-01 9.D-05 9.D-05 7.D-05 5.D-05    6C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6426D+01
 Maximal violation of selected nearly-active constraints        =  1.2994D-03
 Sup-norm of the gradient of the Lagrangian                     =  5.6521D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.8020D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.2369D-06

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.3410D-08
 Sup-norm of the gradient of the Lagrangian                     =  1.6379D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.3410D-08
 Sup-norm of the gradient of the Lagrangian                     =  1.6379D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.2184D-16

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     26
 Subroutine evalg    (coded=T):     21
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     55 (    27 calls per constraint in average)
 Subroutine evaljac  (coded=T):     21 (    10 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 5.4D-04
 Smallest constraints scale factor : 5.5D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         3.411D+03 0.D+00  1.837D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 2.D+01 -8.651D+01 3.D-02 -4.659D-02 2.D-03 2.D-03 8.D-09 6.D-04    6C  0   0
  2 1.D+01 -8.642D+01 4.D-04 -4.655D-02 2.D-05 2.D-05 1.D-05 9.D-06    7C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  4.0176D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.9316D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  5.5715D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.4227D-07

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.2817D-09
 Sup-norm of the gradient of the Lagrangian                     =  5.0632D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  4.6008D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  4.6896D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     26
 Subroutine evalg    (coded=T):     20
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     55 (    27 calls per constraint in average)
 Subroutine evaljac  (coded=T):     17 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      9
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.0D-02
 Smallest constraints scale factor : 5.8D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -8.817D+01 5.D+00 -1.752D+00 3.D+00 3.D+00 1.D+00 3.D+00    0   0   0
  1 4.D+00 -1.170D+02 0.D+00 -2.325D+00 0.D+00 0.D+00 1.D-05 0.D+00    4C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  6.0905D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  3.0452D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.0866D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  2.3183D-10
 Sup-norm of the gradient of the Lagrangian                     =  2.1533D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  7.6131D-15

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.1533D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     12
 Subroutine evalg    (coded=T):     13
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     25 (    12 calls per constraint in average)
 Subroutine evaljac  (coded=T):     11 (     5 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      6
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.170000000000000e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 3.3D-04
 Smallest constraints scale factor : 4.8D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         6.627D+03 0.D+00  2.179D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 2.D+01 -8.648D+01 2.D-02 -2.843D-02 9.D-04 9.D-04 7.D-08 3.D-04    6C  0   0
  2 1.D+01 -8.642D+01 2.D-04 -2.841D-02 1.D-05 1.D-05 4.D-06 4.D-06    7C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  2.2612D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.1140D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.1357D-05
 Sup-norm of the gradient of the Lagrangian                     =  7.9078D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.0596D-10
 Sup-norm of the gradient of the Lagrangian                     =  2.8495D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.5893D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.6290D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     29
 Subroutine evalg    (coded=T):     21
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     63 (    31 calls per constraint in average)
 Subroutine evaljac  (coded=T):     16 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      9
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.2D-02
 Smallest constraints scale factor : 4.5D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -9.388D+01 2.D+00 -2.077D+00 1.D+00 1.D+00 1.D+00 1.D+00    0   0   0
  1 2.D+01 -1.170D+02 0.D+00 -2.589D+00 0.D+00 0.D+00 8.D-08 0.D+00    4C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  3.5375D-06

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  1.7687D-07
 Sup-norm of the gradient of the Lagrangian                     =  7.1054D-14

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  7.9936D-15
 Sup-norm of the gradient of the Lagrangian                     =  3.5817D-15

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  4.4219D-17

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  3.5375D-15

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     11
 Subroutine evalg    (coded=T):     13
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     23 (    11 calls per constraint in average)
 Subroutine evaljac  (coded=T):     11 (     5 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      6
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.170000000000000e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 3.8D-03
 Smallest constraints scale factor : 9.1D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.930D+02 0.D+00  7.331D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.682D+01 1.D-01 -3.298D-01 1.D-02 1.D-02 3.D-05 8.D-03    4C  0   0
  2 1.D+01 -8.644D+01 5.D-03 -3.284D-01 5.D-04 5.D-04 4.D-08 3.D-04    6C  0   0
  3 1.D+01 -8.642D+01 2.D-04 -3.283D-01 2.D-05 2.D-05 1.D-06 1.D-05    7C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  2.1605D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.5939D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  2.9961D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.4105D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.7062D-10
 Sup-norm of the gradient of the Lagrangian                     =  2.7226D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.4740D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.7001D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     27
 Subroutine evalg    (coded=T):     23
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     58 (    29 calls per constraint in average)
 Subroutine evaljac  (coded=T):     22 (    11 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      9
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.5D-02
 Smallest constraints scale factor : 1.0D+00

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -8.725D+01 1.D+01 -2.201D+00 1.D+01 1.D+01 1.D+00 1.D+01    0   0   0
  1 4.D-01 -8.701D+01 2.D-01 -2.195D+00 2.D-01 2.D-01 9.D-10 1.D+00    5C  0   0
  2 2.D+01 -8.642D+01 2.D-04 -2.180D+00 2.D-04 2.D-04 5.D-05 2.D-03    7C  0   0
  3 2.D+01 -8.642D+01 6.D-07 -2.180D+00 6.D-07 6.D-07 4.D-07 4.D-06    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  5.5166D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.5832D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  7.6501D-08
 Sup-norm of the gradient of the Lagrangian                     =  2.1316D-13

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.8874D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.7132D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  6.3166D-16

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.1316D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     22
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     50 (    25 calls per constraint in average)
 Subroutine evaljac  (coded=T):     30 (    15 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 7.7D-02
 Smallest constraints scale factor : 1.5D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -2.203D+01 0.D+00 -1.699D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 2.D+01 -8.792D+01 5.D-01 -6.780D+00 8.D-02 8.D-02 1.D-06 8.D-02    4C  0   0
  2 7.D+01 -8.650D+01 2.D-02 -6.670D+00 4.D-03 4.D-03 2.D-10 4.D-03    7C  0   0
  3 7.D+01 -8.643D+01 1.D-03 -6.664D+00 2.D-04 2.D-04 7.D-10 2.D-04    9C  0   0
  4 7.D+01 -8.642D+01 5.D-05 -6.664D+00 8.D-06 8.D-06 8.D-07 8.D-06   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  5.2459D-05
 Sup-norm of the gradient of the Lagrangian                     =  9.8526D-06

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  7.2748D-06
 Sup-norm of the gradient of the Lagrangian                     =  9.3873D-10

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  2.1849D-11
 Sup-norm of the gradient of the Lagrangian                     =  6.6105D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  6.0068D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  7.4607D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     24
 Subroutine evalg    (coded=T):     29
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     53 (    26 calls per constraint in average)
 Subroutine evaljac  (coded=T):     30 (    15 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     12
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.6D-02
 Smallest constraints scale factor : 3.7D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.782D+02 1.D+01 -4.714D+00 4.D+00 4.D+00 1.D+00 4.D+00    0   0   0
  1 4.D+00 -1.194D+02 2.D-01 -3.159D+00 7.D-02 7.D-02 7.D-12 1.D-01    7C  0   0
  2 3.D+01 -1.187D+02 0.D+00 -3.140D+00 0.D+00 2.D-03 5.D-05 0.D+00   10C  0   0
  3 3.D+01 -1.187D+02 9.D-05 -3.140D+00 3.D-05 3.D-05 2.D-05 5.D-05   11C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  9.2332D-05
 Sup-norm of the gradient of the Lagrangian                     =  6.3432D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.8609D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.2658D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.7939D-10
 Sup-norm of the gradient of the Lagrangian                     =  2.7546D-13

 Maximal violation of constraints                               =  8.8818D-16

 Maximal violation of Lagrange multipliers non-negativity       =  1.1060D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.1058D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     28
 Subroutine evalg    (coded=T):     27
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     62 (    31 calls per constraint in average)
 Subroutine evaljac  (coded=T):     34 (    17 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     13
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.2D-02
 Smallest constraints scale factor : 1.0D+00

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -7.961D+01 1.D+01 -1.761D+00 1.D+01 1.D+01 1.D+00 7.D+00    0   0   0
  1 3.D-01  8.000D+00 0.D+00  1.770D-01 0.D+00 0.D+00 4.D-05 0.D+00    3C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.9988D-03

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  5.9952D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.3962D-07

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  1.4978D-08
 Sup-norm of the gradient of the Lagrangian                     =  1.7307D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  2.6645D-15
 Sup-norm of the gradient of the Lagrangian                     =  7.5024D-17

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     15
 Subroutine evalg    (coded=T):     13
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     31 (    15 calls per constraint in average)
 Subroutine evaljac  (coded=T):     13 (     6 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      6
 Subroutine evalhlp  (coded=F):      0

LOCAL f = 8.000000000000000e+00
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.1D-03
 Smallest constraints scale factor : 9.1D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         9.672D+02 8.D+00  1.069D+00 8.D+00 8.D+00 1.D+00 8.D+00    0   0   0
  1 3.D-01 -1.362D+02 3.D+00 -1.506D-01 2.D-01 2.D-01 6.D-05 8.D-02    4C  0   0
  2 1.D+01 -1.186D+02 0.D+00 -1.311D-01 0.D+00 3.D-03 3.D-05 0.D+00   12C  0   0
  3 1.D+01 -1.187D+02 2.D-03 -1.313D-01 2.D-04 2.D-04 3.D-05 7.D-05   13C  0   0
  4 1.D+01 -1.187D+02 6.D-05 -1.312D-01 5.D-06 5.D-06 3.D-07 2.D-06   14C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  5.6936D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.4400D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2993D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.2594D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.3606D-09
 Sup-norm of the gradient of the Lagrangian                     =  5.2166D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.0944D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.2560D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     38
 Subroutine evalg    (coded=T):     34
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     85 (    42 calls per constraint in average)
 Subroutine evaljac  (coded=T):     43 (    21 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     16
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.3D-03
 Smallest constraints scale factor : 8.1D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         4.157D+02 0.D+00  9.430D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.673D+01 1.D-01 -1.967D-01 8.D-03 8.D-03 2.D-09 5.D-03    5C  0   0
  2 1.D+01 -8.643D+01 3.D-03 -1.961D-01 3.D-04 3.D-04 1.D-08 1.D-04    7C  0   0
  3 1.D+01 -8.642D+01 1.D-04 -1.960D-01 8.D-06 8.D-06 3.D-07 5.D-06    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  9.6255D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.1606D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.3348D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.7977D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  7.3559D-11
 Sup-norm of the gradient of the Lagrangian                     =  1.2129D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.1022D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.2079D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     30
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     64 (    32 calls per constraint in average)
 Subroutine evaljac  (coded=T):     22 (    11 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.3D-03
 Smallest constraints scale factor : 8.1D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         4.566D+02 0.D+00  1.030D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.672D+01 1.D-01 -1.956D-01 8.D-03 8.D-03 4.D-09 5.D-03    7C  0   0
  2 1.D+01 -8.643D+01 3.D-03 -1.950D-01 2.D-04 2.D-04 9.D-09 1.D-04    9C  0   0
  3 1.D+01 -8.642D+01 9.D-05 -1.950D-01 7.D-06 7.D-06 2.D-07 4.D-06   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  9.2787D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.0871D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.2867D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.6111D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  6.8355D-11
 Sup-norm of the gradient of the Lagrangian                     =  1.1692D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.0625D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.2434D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     33
 Subroutine evalg    (coded=T):     27
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     72 (    36 calls per constraint in average)
 Subroutine evaljac  (coded=T):     23 (    11 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     12
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.1D-03
 Smallest constraints scale factor : 9.1D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         9.767D+02 6.D+00  1.094D+00 6.D+00 6.D+00 1.D+00 6.D+00    0   0   0
  1 5.D-01 -1.319D+02 2.D+00 -1.477D-01 2.D-01 2.D-01 1.D-05 7.D-02    4C  0   0
  2 1.D+01 -1.185D+02 0.D+00 -1.327D-01 0.D+00 6.D-03 2.D-06 0.D+00   11C  0   0
  3 1.D+01 -1.187D+02 3.D-03 -1.330D-01 3.D-04 3.D-04 3.D-07 1.D-04   14C  0   0
  4 1.D+01 -1.187D+02 0.D+00 -1.330D-01 0.D+00 1.D-05 2.D-07 0.D+00   15C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  6.2000D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-07

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-06

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-05

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-04

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-03

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-01

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+00

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+01

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.0796D-06
 Sup-norm of the gradient of the Lagrangian                     =  6.2000D-02

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.0625D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.6099D-02

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.8754D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.4279D-02

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.2488D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.5490D-02

 NEWTON-KKT Iteration                                           =           5
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7964D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.8955D-02

 NEWTON-KKT Iteration                                           =           6
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  9.9332D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.4095D-02

 NEWTON-KKT Iteration                                           =           7
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.4924D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.0481D-02

 NEWTON-KKT Iteration                                           =           8
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.0368D-07
 Sup-norm of the gradient of the Lagrangian                     =  7.7935D-03

 NEWTON-KKT Iteration                                           =           9
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.6791D-07
 Sup-norm of the gradient of the Lagrangian                     =  5.7952D-03

 NEWTON-KKT Iteration                                           =          10
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  9.2836D-08
 Sup-norm of the gradient of the Lagrangian                     =  4.3092D-03

 Flag of NEWTON-KKT = Maximum of iterations reached.
  5 1.D+01 -1.187D+02 9.D-06 -1.330D-01 8.D-07 8.D-07 5.D-10 3.D-07   16C  1  10

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  9.3361D-06
 Sup-norm of the gradient of the Lagrangian                     =  4.4240D-07

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.8250D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.3681D-12

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.6844D-11
 Sup-norm of the gradient of the Lagrangian                     =  7.3273D-14

 Maximal violation of constraints                               =  6.6613D-16

 Maximal violation of Lagrange multipliers non-negativity       =  2.9418D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.9310D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     52
 Subroutine evalg    (coded=T):     51
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):    113 (    56 calls per constraint in average)
 Subroutine evaljac  (coded=T):     70 (    35 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     28
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.6D-03
 Smallest constraints scale factor : 1.0D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         5.093D+02 4.D+00  8.320D-01 4.D+00 4.D+00 1.D+00 4.D+00    0   0   0
  1 1.D+00 -1.238D+02 1.D+00 -2.023D-01 1.D-01 1.D-01 2.D-06 5.D-02    5C  0   0
  2 1.D+01 -1.185D+02 0.D+00 -1.936D-01 0.D+00 6.D-03 7.D-05 0.D+00   10C  0   0
  3 1.D+01 -1.187D+02 4.D-03 -1.939D-01 4.D-04 4.D-04 5.D-08 2.D-04   13C  0   0
  4 1.D+01 -1.187D+02 0.D+00 -1.939D-01 0.D+00 2.D-05 3.D-07 0.D+00   14C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  7.0446D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-07

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-06

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-05

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-04

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-03

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-01

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+00

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+01

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.6688D-06
 Sup-norm of the gradient of the Lagrangian                     =  7.0446D-02

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.3716D-05
 Sup-norm of the gradient of the Lagrangian                     =  5.2379D-02

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.5854D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.8950D-02

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.1946D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.8964D-02

 NEWTON-KKT Iteration                                           =           5
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.3195D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.1538D-02

 NEWTON-KKT Iteration                                           =           6
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2826D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.6016D-02

 NEWTON-KKT Iteration                                           =           7
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.0922D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.1910D-02

 NEWTON-KKT Iteration                                           =           8
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.9215D-07
 Sup-norm of the gradient of the Lagrangian                     =  8.8563D-03

 NEWTON-KKT Iteration                                           =           9
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.1683D-07
 Sup-norm of the gradient of the Lagrangian                     =  6.5855D-03

 NEWTON-KKT Iteration                                           =          10
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.1989D-07
 Sup-norm of the gradient of the Lagrangian                     =  4.8970D-03

 Flag of NEWTON-KKT = Maximum of iterations reached.
  5 1.D+01 -1.187D+02 1.D-05 -1.939D-01 1.D-06 1.D-06 1.D-09 6.D-07   15C  1  10

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.3543D-05
 Sup-norm of the gradient of the Lagrangian                     =  6.5112D-07

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.6474D-05
 Sup-norm of the gradient of the Lagrangian                     =  9.2406D-12

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.6489D-11
 Sup-norm of the gradient of the Lagrangian                     =  1.0629D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  4.2675D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  7.9936D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     48
 Subroutine evalg    (coded=T):     48
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):    100 (    50 calls per constraint in average)
 Subroutine evaljac  (coded=T):     61 (    30 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     27
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 8.7D-02
 Smallest constraints scale factor : 2.8D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -2.255D+01 6.D+00 -1.962D+00 2.D+00 2.D+00 1.D+00 2.D+00    0   0   0
  1 2.D+01 -8.701D+01 2.D-01 -7.568D+00 6.D-02 6.D-02 8.D-07 1.D-01    5C  0   0
  2 8.D+01 -8.643D+01 3.D-03 -7.517D+00 7.D-04 7.D-04 1.D-05 1.D-03    7C  0   0
  3 8.D+01 -8.642D+01 3.D-05 -7.516D+00 9.D-06 9.D-06 2.D-05 2.D-05    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.2827D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.9012D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.5524D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.3529D-10

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  8.5559D-12
 Sup-norm of the gradient of the Lagrangian                     =  4.1367D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  3.7589D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  3.9080D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     25
 Subroutine evalg    (coded=T):     25
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     57 (    28 calls per constraint in average)
 Subroutine evaljac  (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 5.5D-03
 Smallest constraints scale factor : 1.5D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.225D+01 0.D+00 -6.710D-02 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -1.190D+02 9.D-02 -6.520D-01 1.D-02 1.D-02 2.D-05 9.D-03    9C  0   0
  2 1.D+01 -1.187D+02 0.D+00 -6.500D-01 0.D+00 1.D-03 1.D-05 0.D+00   11C  0   0
  3 1.D+01 -1.187D+02 8.D-04 -6.501D-01 1.D-04 1.D-04 4.D-06 8.D-05   12C  0   0
  4 1.D+01 -1.187D+02 0.D+00 -6.501D-01 0.D+00 1.D-05 3.D-08 0.D+00   13C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  4.1418D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  2.8455D-04
 Sup-norm of the gradient of the Lagrangian                     =  6.2148D-06

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.4027D-09
 Sup-norm of the gradient of the Lagrangian                     =  4.6388D-10

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     40
 Subroutine evalg    (coded=T):     35
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     91 (    45 calls per constraint in average)
 Subroutine evaljac  (coded=T):     30 (    15 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     15
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597744077e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.5D-02
 Smallest constraints scale factor : 1.3D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         2.185D+01 0.D+00  5.489D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.757D+01 4.D-01 -2.200D+00 5.D-02 5.D-02 2.D-06 5.D-02    4C  0   0
  2 2.D+01 -8.649D+01 2.D-02 -2.173D+00 3.D-03 3.D-03 1.D-05 3.D-03    6C  0   0
  3 2.D+01 -8.643D+01 1.D-03 -2.171D+00 2.D-04 2.D-04 8.D-05 2.D-04    7C  0   0
  4 2.D+01 -8.642D+01 1.D-04 -2.171D+00 1.D-05 1.D-05 3.D-07 1.D-05    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  1.1160D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.0651D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.5476D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.2926D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  9.8880D-11
 Sup-norm of the gradient of the Lagrangian                     =  1.4063D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.2779D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.3145D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     23
 Subroutine evalg    (coded=T):     27
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     51 (    25 calls per constraint in average)
 Subroutine evaljac  (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 5.4D-04
 Smallest constraints scale factor : 5.6D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         3.392D+03 0.D+00  1.835D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 2.D+01 -8.651D+01 3.D-02 -4.681D-02 2.D-03 2.D-03 5.D-09 6.D-04    6C  0   0
  2 1.D+01 -8.642D+01 4.D-04 -4.677D-02 2.D-05 2.D-05 1.D-05 9.D-06    7C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  4.0372D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.9389D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  5.5986D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.4430D-07

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.2942D-09
 Sup-norm of the gradient of the Lagrangian                     =  5.0878D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  4.6232D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  4.7606D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     31
 Subroutine evalg    (coded=T):     22
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     69 (    34 calls per constraint in average)
 Subroutine evaljac  (coded=T):     17 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      9
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 9.9D-02
 Smallest constraints scale factor : 2.1D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.565D+01 2.D+00 -1.544D+00 5.D-01 5.D-01 1.D+00 5.D-01    0   0   0
  1 2.D+01 -8.753D+01 4.D-01 -8.634D+00 8.D-02 8.D-02 3.D-06 1.D-01    4C  0   0
  2 9.D+01 -8.645D+01 9.D-03 -8.527D+00 2.D-03 2.D-03 4.D-11 3.D-03    7C  0   0
  3 9.D+01 -8.642D+01 2.D-04 -8.525D+00 4.D-05 4.D-05 3.D-11 7.D-05    9C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  2.0824D-04
 Sup-norm of the gradient of the Lagrangian                     =  3.3150D-10

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  2.8877D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.5106D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.4429D-10
 Sup-norm of the gradient of the Lagrangian                     =  2.6241D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.3845D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.5580D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     24
 Subroutine evalg    (coded=T):     25
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     53 (    26 calls per constraint in average)
 Subroutine evaljac  (coded=T):     28 (    14 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     11
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 8.7D-02
 Smallest constraints scale factor : 2.4D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -2.241D+01 5.D+00 -1.940D+00 1.D+00 1.D+00 1.D+00 1.D+00    0   0   0
  1 2.D+01 -8.705D+01 2.D-01 -7.534D+00 5.D-02 5.D-02 1.D-07 9.D-02    7C  0   0
  2 8.D+01 -8.643D+01 4.D-03 -7.480D+00 9.D-04 9.D-04 1.D-05 2.D-03    9C  0   0
  3 8.D+01 -8.642D+01 7.D-05 -7.479D+00 2.D-05 2.D-05 3.D-05 3.D-05   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  6.5371D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.9610D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  9.0653D-06
 Sup-norm of the gradient of the Lagrangian                     =  7.4406D-10

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.3929D-11
 Sup-norm of the gradient of the Lagrangian                     =  8.2376D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  7.4853D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  7.8160D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     30
 Subroutine evalg    (coded=T):     27
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     67 (    33 calls per constraint in average)
 Subroutine evaljac  (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     12
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.4D-02
 Smallest constraints scale factor : 1.0D+00

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.058D+02 1.D+01 -2.590D+00 1.D+01 1.D+01 1.D+00 1.D+01    0   0   0
  1 3.D-01 -8.701D+01 2.D-01 -2.131D+00 2.D-01 2.D-01 3.D-09 1.D+00    6C  0   0
  2 2.D+01 -8.642D+01 2.D-04 -2.116D+00 2.D-04 2.D-04 5.D-05 2.D-03    8C  0   0
  3 2.D+01 -8.642D+01 5.D-07 -2.116D+00 5.D-07 5.D-07 4.D-07 4.D-06    9C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  5.4326D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.5664D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  7.5337D-08
 Sup-norm of the gradient of the Lagrangian                     =  2.1672D-13

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.8874D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.7142D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  6.2205D-16

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.1316D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     26
 Subroutine evalg    (coded=T):     25
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     58 (    29 calls per constraint in average)
 Subroutine evaljac  (coded=T):     30 (    15 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     11
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.4D-02
 Smallest constraints scale factor : 1.0D+00

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.094D+02 1.D+01 -2.666D+00 1.D+01 1.D+01 1.D+00 1.D+01    0   0   0
  1 3.D-01 -8.701D+01 2.D-01 -2.121D+00 2.D-01 2.D-01 2.D-07 1.D+00    7C  0   0
  2 2.D+01 -8.642D+01 2.D-04 -2.107D+00 2.D-04 2.D-04 5.D-05 2.D-03    9C  0   0
  3 2.D+01 -8.642D+01 5.D-07 -2.107D+00 5.D-07 5.D-07 4.D-07 4.D-06   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  5.4375D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.5675D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  7.5405D-08
 Sup-norm of the gradient of the Lagrangian                     =  2.1316D-13

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.8874D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.3588D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  6.2262D-16

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.4211D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     24
 Subroutine evalg    (coded=T):     25
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     52 (    26 calls per constraint in average)
 Subroutine evaljac  (coded=T):     30 (    15 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     12
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.3D-03
 Smallest constraints scale factor : 9.5D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         8.112D+02 3.D+00  1.036D+00 3.D+00 3.D+00 1.D+00 3.D+00    0   0   0
  1 2.D+00 -1.204D+02 4.D-01 -1.538D-01 4.D-02 4.D-02 3.D-05 2.D-02    5C  0   0
  2 1.D+01 -1.186D+02 0.D+00 -1.515D-01 0.D+00 2.D-03 2.D-05 0.D+00    8C  0   0
  3 1.D+01 -1.187D+02 1.D-03 -1.516D-01 1.D-04 1.D-04 1.D-05 4.D-05    9C  0   0
  4 1.D+01 -1.187D+02 0.D+00 -1.516D-01 0.D+00 1.D-06 9.D-09 0.D+00   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.7698D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.1039D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.7690D-07

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  8.0038D-11
 Sup-norm of the gradient of the Lagrangian                     =  8.2284D-12

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     23
 Subroutine evalg    (coded=T):     28
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     50 (    25 calls per constraint in average)
 Subroutine evaljac  (coded=T):     33 (    16 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     12
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749746e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.0D-02
 Smallest constraints scale factor : 6.2D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.240D+02 9.D+00 -2.431D+00 6.D+00 6.D+00 1.D+00 5.D+00    0   0   0
  1 2.D+00 -1.192D+02 1.D-01 -2.337D+00 8.D-02 8.D-02 9.D-05 2.D-01    6C  0   0
  2 2.D+01 -1.187D+02 0.D+00 -2.328D+00 0.D+00 6.D-04 8.D-05 0.D+00    9C  0   0
  3 2.D+01 -1.187D+02 8.D-06 -2.328D+00 5.D-06 5.D-06 2.D-06 1.D-05   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  8.3028D-06
 Sup-norm of the gradient of the Lagrangian                     =  9.5358D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.0551D-06
 Sup-norm of the gradient of the Lagrangian                     =  5.6740D-11

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  8.9706D-14
 Sup-norm of the gradient of the Lagrangian                     =  5.1590D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.7008D-15

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  5.3291D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     29
 Subroutine evalg    (coded=T):     27
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     66 (    33 calls per constraint in average)
 Subroutine evaljac  (coded=T):     35 (    17 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     12
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.2D-03
 Smallest constraints scale factor : 6.9D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.152D+03 0.D+00  1.375D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.659D+01 5.D-02 -1.033D-01 4.D-03 4.D-03 4.D-06 2.D-03    5C  0   0
  2 1.D+01 -8.643D+01 1.D-03 -1.032D-01 7.D-05 7.D-05 6.D-05 4.D-05    6C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6425D+01
 Maximal violation of selected nearly-active constraints        =  1.0746D-03
 Sup-norm of the gradient of the Lagrangian                     =  4.9037D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.4903D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.6247D-06

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  9.1715D-09
 Sup-norm of the gradient of the Lagrangian                     =  1.3545D-12

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.2308D-12

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.2612D-12

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     30
 Subroutine evalg    (coded=T):     20
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     65 (    32 calls per constraint in average)
 Subroutine evaljac  (coded=T):     16 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      8
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 8.9D-04
 Smallest constraints scale factor : 8.4D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.446D+03 6.D+00  1.286D+00 6.D+00 6.D+00 1.D+00 6.D+00    0   0   0
  1 8.D-01 -1.253D+02 1.D+00 -1.115D-01 1.D-01 1.D-01 4.D-08 4.D-02    6C  0   0
  2 1.D+01 -1.185D+02 0.D+00 -1.055D-01 0.D+00 5.D-03 5.D-05 0.D+00   12C  0   0
  3 1.D+01 -1.187D+02 2.D-03 -1.056D-01 2.D-04 2.D-04 9.D-05 7.D-05   14C  0   0
  4 1.D+01 -1.187D+02 4.D-04 -1.056D-01 3.D-05 3.D-05 2.D-07 1.D-05   15C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  3.8361D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.0585D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.6982D-04
 Sup-norm of the gradient of the Lagrangian                     =  7.9981D-10

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.7774D-08
 Sup-norm of the gradient of the Lagrangian                     =  3.0915D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.7774D-08
 Sup-norm of the gradient of the Lagrangian                     =  3.0915D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  3.4757D-15

 Maximal violation of constraints                               =  8.8818D-16

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     36
 Subroutine evalg    (coded=T):     35
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     75 (    37 calls per constraint in average)
 Subroutine evaljac  (coded=T):     40 (    20 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     19
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 5.4D-03
 Smallest constraints scale factor : 9.8D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.458D+02 0.D+00  7.828D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.690D+01 2.D-01 -4.667D-01 2.D-02 2.D-02 7.D-09 1.D-02    6C  0   0
  2 1.D+01 -8.645D+01 8.D-03 -4.642D-01 8.D-04 8.D-04 9.D-08 5.D-04    8C  0   0
  3 1.D+01 -8.642D+01 4.D-04 -4.641D-01 4.D-05 4.D-05 2.D-06 3.D-05    9C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  3.7577D-04
 Sup-norm of the gradient of the Lagrangian                     =  4.4184D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  5.2111D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.2804D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.1212D-09
 Sup-norm of the gradient of the Lagrangian                     =  4.7356D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  4.3031D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  4.3343D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     21
 Subroutine evalg    (coded=T):     25
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     46 (    23 calls per constraint in average)
 Subroutine evaljac  (coded=T):     23 (    11 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     11
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 4.4D-02
 Smallest constraints scale factor : 4.8D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.077D+01 5.D+00 -4.757D-01 3.D+00 3.D+00 1.D+00 3.D+00    0   0   0
  1 3.D+00  8.000D+00 0.D+00  3.534D-01 0.D+00 0.D+00 5.D-05 0.D+00    6C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.0396D-03

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  3.1183D-04
 Sup-norm of the gradient of the Lagrangian                     =  6.4827D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       =  8.0000D+00
 Maximal violation of selected nearly-active constraints        =  4.0520D-09
 Sup-norm of the gradient of the Lagrangian                     =  9.0019D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     13
 Subroutine evalg    (coded=T):     15
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evaljac  (coded=T):     11 (     5 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      8
 Subroutine evalhlp  (coded=F):      0

LOCAL f = 8.000000000000000e+00
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.8D-03
 Smallest constraints scale factor : 8.5D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         3.607D+02 0.D+00  1.001D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.676D+01 1.D-01 -2.406D-01 1.D-02 1.D-02 3.D-06 6.D-03    5C  0   0
  2 1.D+01 -8.643D+01 4.D-03 -2.397D-01 3.D-04 3.D-04 2.D-08 2.D-04    7C  0   0
  3 1.D+01 -8.642D+01 1.D-04 -2.397D-01 1.D-05 1.D-05 4.D-07 7.D-06    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  1.3091D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.5725D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.8154D-05
 Sup-norm of the gradient of the Lagrangian                     =  5.1782D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.3607D-10
 Sup-norm of the gradient of the Lagrangian                     =  1.6497D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.4990D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.6342D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     21
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     46 (    23 calls per constraint in average)
 Subroutine evaljac  (coded=T):     21 (    10 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.9D-02
 Smallest constraints scale factor : 2.0D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.713D+02 5.D+00 -3.202D+00 4.D+00 4.D+00 1.D+00 4.D+00    0   0   0
  1 4.D+00 -1.218D+02 7.D-01 -2.275D+00 1.D-01 1.D-01 2.D-07 1.D-01    4C  0   0
  2 2.D+01 -1.185D+02 0.D+00 -2.215D+00 0.D+00 1.D-02 5.D-06 0.D+00    8C  0   0
  3 2.D+01 -1.187D+02 4.D-03 -2.218D+00 9.D-04 9.D-04 5.D-06 8.D-04   10C  0   0
  4 2.D+01 -1.187D+02 0.D+00 -2.218D+00 0.D+00 7.D-05 4.D-06 0.D+00   11C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  9.2958D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-07

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-06

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-05

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-04

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-03

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-01

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+00

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+01

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.3217D-06
 Sup-norm of the gradient of the Lagrangian                     =  9.2958D-02

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.3881D-05
 Sup-norm of the gradient of the Lagrangian                     =  6.9117D-02

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.3210D-05
 Sup-norm of the gradient of the Lagrangian                     =  5.1400D-02

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.3058D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.8225D-02

 NEWTON-KKT Iteration                                           =           5
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.0405D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.8427D-02

 NEWTON-KKT Iteration                                           =           6
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.2346D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.1140D-02

 NEWTON-KKT Iteration                                           =           7
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2357D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.5721D-02

 NEWTON-KKT Iteration                                           =           8
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.8335D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.1691D-02

 NEWTON-KKT Iteration                                           =           9
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.7787D-07
 Sup-norm of the gradient of the Lagrangian                     =  8.6939D-03

 NEWTON-KKT Iteration                                           =          10
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.0894D-07
 Sup-norm of the gradient of the Lagrangian                     =  6.4650D-03

 Flag of NEWTON-KKT = Maximum of iterations reached.
  5 2.D+01 -1.187D+02 3.D-05 -2.218D+00 6.D-06 6.D-06 3.D-08 5.D-06   12C  1  10

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.1924D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.4388D-06

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.2434D-05
 Sup-norm of the gradient of the Lagrangian                     =  5.1463D-11

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.1417D-10
 Sup-norm of the gradient of the Lagrangian                     =  2.5067D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.0064D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.2434D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     39
 Subroutine evalg    (coded=T):     45
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     84 (    42 calls per constraint in average)
 Subroutine evaljac  (coded=T):     63 (    31 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     24
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.8D-03
 Smallest constraints scale factor : 1.1D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         4.606D+02 0.D+00  8.292D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -1.189D+02 6.D-02 -2.140D-01 6.D-03 6.D-03 7.D-06 3.D-03    8C  0   0
  2 1.D+01 -1.187D+02 0.D+00 -2.137D-01 0.D+00 4.D-04 9.D-05 0.D+00    9C  0   0
  3 1.D+01 -1.187D+02 7.D-04 -2.137D-01 7.D-05 7.D-05 1.D-06 3.D-05   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  7.0078D-04
 Sup-norm of the gradient of the Lagrangian                     =  8.1017D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2277D-03
 Sup-norm of the gradient of the Lagrangian                     =  2.2507D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2153D-07
 Sup-norm of the gradient of the Lagrangian                     =  4.9311D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2153D-07
 Sup-norm of the gradient of the Lagrangian                     =  4.9311D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.0854D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     33
 Subroutine evalg    (coded=T):     29
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     72 (    36 calls per constraint in average)
 Subroutine evaljac  (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     14
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 4.3D-02
 Smallest constraints scale factor : 4.8D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -8.391D+01 1.D+01 -3.638D+00 5.D+00 5.D+00 1.D+00 7.D+00    0   0   0
  1 2.D+00 -8.706D+01 2.D-01 -3.774D+00 1.D-01 1.D-01 9.D-07 4.D-01    5C  0   0
  2 4.D+01 -8.643D+01 1.D-03 -3.747D+00 5.D-04 5.D-04 3.D-05 2.D-03    7C  0   0
  3 4.D+01 -8.642D+01 5.D-06 -3.746D+00 2.D-06 2.D-06 3.D-06 8.D-06    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.7069D-06
 Sup-norm of the gradient of the Lagrangian                     =  7.8887D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  6.5273D-07
 Sup-norm of the gradient of the Lagrangian                     =  6.5583D-12

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.7530D-13
 Sup-norm of the gradient of the Lagrangian                     =  5.9312D-15

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  5.3895D-15

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.0658D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     26
 Subroutine evalg    (coded=T):     25
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     60 (    30 calls per constraint in average)
 Subroutine evaljac  (coded=T):     30 (    15 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.0D-02
 Smallest constraints scale factor : 6.2D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.394D+02 1.D+01 -2.736D+00 6.D+00 6.D+00 1.D+00 5.D+00    0   0   0
  1 1.D+00 -1.193D+02 2.D-01 -2.341D+00 1.D-01 1.D-01 2.D-06 3.D-01    7C  0   0
  2 2.D+01 -1.187D+02 0.D+00 -2.330D+00 0.D+00 8.D-04 4.D-07 0.D+00   11C  0   0
  3 2.D+01 -1.187D+02 1.D-05 -2.330D+00 6.D-06 6.D-06 3.D-06 2.D-05   12C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  9.8347D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.5690D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  9.0436D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.3657D-10

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.5925D-12
 Sup-norm of the gradient of the Lagrangian                     =  5.6477D-14

 Maximal violation of constraints                               =  1.3323D-15

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     28
 Subroutine evalg    (coded=T):     28
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     62 (    31 calls per constraint in average)
 Subroutine evaljac  (coded=T):     37 (    18 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     14
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 4.3D-02
 Smallest constraints scale factor : 1.4D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.713D+01 0.D+00 -7.433D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.801D+01 6.D-01 -3.819D+00 8.D-02 8.D-02 3.D-08 8.D-02    6C  0   0
  2 4.D+01 -8.651D+01 3.D-02 -3.754D+00 4.D-03 4.D-03 2.D-10 4.D-03    9C  0   0
  3 4.D+01 -8.643D+01 2.D-03 -3.750D+00 2.D-04 2.D-04 7.D-10 2.D-04   11C  0   0
  4 4.D+01 -8.642D+01 8.D-05 -3.750D+00 1.D-05 1.D-05 7.D-07 1.D-05   12C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  7.7712D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.5369D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.0777D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.0577D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.7948D-11
 Sup-norm of the gradient of the Lagrangian                     =  9.7928D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  8.8985D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  8.8818D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     26
 Subroutine evalg    (coded=T):     30
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     55 (    27 calls per constraint in average)
 Subroutine evaljac  (coded=T):     29 (    14 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     14
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.7D-03
 Smallest constraints scale factor : 1.0D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         4.280D+02 7.D+00  7.295D-01 7.D+00 7.D+00 1.D+00 7.D+00    0   0   0
  1 4.D-01 -1.351D+02 2.D+00 -2.304D-01 3.D-01 3.D-01 3.D-06 1.D-01    3C  0   0
  2 1.D+01 -1.170D+02 0.D+00 -1.994D-01 0.D+00 0.D+00 2.D-07 0.D+00    9C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.2323D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  6.1616D-06
 Sup-norm of the gradient of the Lagrangian                     =  8.5464D-11

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1700D+02
 Maximal violation of selected nearly-active constraints        =  9.4911D-12
 Sup-norm of the gradient of the Lagrangian                     =  4.3569D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.5404D-15

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  4.3569D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     30
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     69 (    34 calls per constraint in average)
 Subroutine evaljac  (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     11
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.170000000000000e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.9D-04
 Smallest constraints scale factor : 4.7D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         7.826D+03 0.D+00  2.273D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 2.D+01 -8.648D+01 2.D-02 -2.512D-02 8.D-04 8.D-04 8.D-06 3.D-04    7C  0   0
  2 1.D+01 -8.642D+01 2.D-04 -2.510D-02 1.D-05 1.D-05 3.D-06 4.D-06    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  2.4741D-04
 Sup-norm of the gradient of the Lagrangian                     =  9.6409D-03

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.4310D-05
 Sup-norm of the gradient of the Lagrangian                     =  7.0420D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.8601D-10
 Sup-norm of the gradient of the Lagrangian                     =  3.1178D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.8331D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  3.0198D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     36
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     81 (    40 calls per constraint in average)
 Subroutine evaljac  (coded=T):     16 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.3D-03
 Smallest constraints scale factor : 1.2D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.912D+02 7.D+00  4.433D-01 7.D+00 7.D+00 1.D+00 7.D+00    0   0   0
  1 4.D-01 -1.358D+02 3.D+00 -3.149D-01 3.D-01 3.D-01 9.D-06 1.D-01    4C  0   0
  2 1.D+01 -1.185D+02 0.D+00 -2.748D-01 0.D+00 6.D-03 4.D-05 0.D+00   11C  0   0
  3 1.D+01 -1.187D+02 4.D-03 -2.752D-01 4.D-04 4.D-04 9.D-05 2.D-04   12C  0   0
  4 1.D+01 -1.187D+02 8.D-05 -2.752D-01 9.D-06 9.D-06 1.D-06 5.D-06   13C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  8.1048D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.1993D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.9290D-04
 Sup-norm of the gradient of the Lagrangian                     =  3.4004D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.9992D-09
 Sup-norm of the gradient of the Lagrangian                     =  7.7452D-13

 Maximal violation of constraints                               =  1.7764D-15

 Maximal violation of Lagrange multipliers non-negativity       =  3.1096D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  3.1086D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     32
 Subroutine evalg    (coded=T):     32
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     71 (    35 calls per constraint in average)
 Subroutine evaljac  (coded=T):     43 (    21 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     15
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 6.6D-03
 Smallest constraints scale factor : 1.0D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.008D+02 0.D+00  6.667D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.696D+01 2.D-01 -5.753D-01 2.D-02 2.D-02 2.D-08 1.D-02    5C  0   0
  2 1.D+01 -8.645D+01 1.D-02 -5.719D-01 1.D-03 1.D-03 2.D-07 7.D-04    7C  0   0
  3 1.D+01 -8.642D+01 5.D-04 -5.717D-01 5.D-05 5.D-05 4.D-06 4.D-05    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6424D+01
 Maximal violation of selected nearly-active constraints        =  5.3252D-04
 Sup-norm of the gradient of the Lagrangian                     =  6.2078D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  7.3849D-05
 Sup-norm of the gradient of the Lagrangian                     =  8.6076D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  2.2518D-09
 Sup-norm of the gradient of the Lagrangian                     =  6.7112D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  6.0984D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  6.1107D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     26
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     56 (    28 calls per constraint in average)
 Subroutine evaljac  (coded=T):     22 (    11 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 7.4D-04
 Smallest constraints scale factor : 6.0D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         2.215D+03 0.D+00  1.634D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 2.D+01 -8.653D+01 4.D-02 -6.384D-02 2.D-03 2.D-03 2.D-08 1.D-03    6C  0   0
  2 1.D+01 -8.642D+01 6.D-04 -6.376D-02 4.D-05 4.D-05 2.D-05 2.D-05    7C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6424D+01
 Maximal violation of selected nearly-active constraints        =  5.8862D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.7997D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  8.1628D-05
 Sup-norm of the gradient of the Lagrangian                     =  5.1317D-07

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  2.7512D-09
 Sup-norm of the gradient of the Lagrangian                     =  7.4183D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  6.7408D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  6.7502D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     29
 Subroutine evalg    (coded=T):     21
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     63 (    31 calls per constraint in average)
 Subroutine evaljac  (coded=T):     17 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      9
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.0D-02
 Smallest constraints scale factor : 1.0D+00

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -5.493D+01 6.D+00 -1.085D+00 6.D+00 6.D+00 1.D+00 3.D+00    0   0   0
  1 6.D-01 -1.191D+02 1.D-01 -2.353D+00 1.D-01 1.D-01 2.D-08 5.D-01    6C  0   0
  2 2.D+01 -1.187D+02 0.D+00 -2.345D+00 0.D+00 4.D-04 4.D-06 0.D+00   10C  0   0
  3 2.D+01 -1.187D+02 1.D-06 -2.345D+00 1.D-06 1.D-06 7.D-07 5.D-06   11C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.1206D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.4559D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.0471D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.3994D-11

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.3189D-12
 Sup-norm of the gradient of the Lagrangian                     =  2.0122D-14

 Maximal violation of constraints                               =  1.3323D-15

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     27
 Subroutine evalg    (coded=T):     27
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     59 (    29 calls per constraint in average)
 Subroutine evaljac  (coded=T):     29 (    14 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     13
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.9D-02
 Smallest constraints scale factor : 2.1D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.829D+02 6.D+00 -5.224D+00 4.D+00 4.D+00 1.D+00 4.D+00    0   0   0
  1 6.D+00 -1.212D+02 6.D-01 -3.461D+00 1.D-01 1.D-01 6.D-07 1.D-01    5C  0   0
  2 3.D+01 -1.186D+02 0.D+00 -3.387D+00 0.D+00 8.D-03 2.D-06 0.D+00    9C  0   0
  3 3.D+01 -1.187D+02 3.D-03 -3.391D+00 6.D-04 6.D-04 3.D-06 5.D-04   11C  0   0
  4 3.D+01 -1.187D+02 0.D+00 -3.391D+00 0.D+00 4.D-05 3.D-06 0.D+00   12C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.4409D-16
 Sup-norm of the gradient of the Lagrangian                     =  6.9322D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-07

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-06

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-05

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-04

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-03

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-01

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+00

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+01

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.9333D-06
 Sup-norm of the gradient of the Lagrangian                     =  6.9322D-02

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.3282D-05
 Sup-norm of the gradient of the Lagrangian                     =  5.1544D-02

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.3453D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.8328D-02

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.0618D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.8502D-02

 NEWTON-KKT Iteration                                           =           5
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.2460D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.1194D-02

 NEWTON-KKT Iteration                                           =           6
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2420D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.5760D-02

 NEWTON-KKT Iteration                                           =           7
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.8674D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.1720D-02

 NEWTON-KKT Iteration                                           =           8
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.7972D-07
 Sup-norm of the gradient of the Lagrangian                     =  8.7147D-03

 NEWTON-KKT Iteration                                           =           9
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.0995D-07
 Sup-norm of the gradient of the Lagrangian                     =  6.4803D-03

 NEWTON-KKT Iteration                                           =          10
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.1608D-07
 Sup-norm of the gradient of the Lagrangian                     =  4.8186D-03

 Flag of NEWTON-KKT = Maximum of iterations reached.
  5 3.D+01 -1.187D+02 2.D-05 -3.391D+00 3.D-06 3.D-06 1.D-08 3.D-06   13C  1  10

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.5285D-05
 Sup-norm of the gradient of the Lagrangian                     =  5.0292D-07

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.9934D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.1626D-11

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.2219D-11
 Sup-norm of the gradient of the Lagrangian                     =  1.2018D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  4.8253D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  4.8406D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     40
 Subroutine evalg    (coded=T):     46
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     86 (    43 calls per constraint in average)
 Subroutine evaljac  (coded=T):     65 (    32 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     25
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 9.7D-02
 Smallest constraints scale factor : 2.4D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.245D+02 1.D+00 -1.206D+01 3.D-01 3.D-01 1.D+00 3.D-01    0   0   0
  1 1.D+02 -1.189D+02 5.D-02 -1.151D+01 1.D-02 1.D-02 5.D-09 1.D-02    6C  0   0
  2 1.D+02 -1.187D+02 0.D+00 -1.149D+01 0.D+00 7.D-04 9.D-05 0.D+00    8C  0   0
  3 1.D+02 -1.187D+02 2.D-04 -1.150D+01 4.D-05 4.D-05 1.D-05 4.D-05    9C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  1.7479D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.3470D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.1817D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.7148D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  8.1600D-09
 Sup-norm of the gradient of the Lagrangian                     =  1.2776D-12

 Maximal violation of constraints                               =  1.5543D-15

 Maximal violation of Lagrange multipliers non-negativity       =  5.1293D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  5.1292D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     28
 Subroutine evalg    (coded=T):     25
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     61 (    30 calls per constraint in average)
 Subroutine evaljac  (coded=T):     28 (    14 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     11
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 5.6D-04
 Smallest constraints scale factor : 7.2D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         2.930D+03 3.D+00  1.654D+00 3.D+00 3.D+00 1.D+00 3.D+00    0   0   0
  1 3.D+00 -1.192D+02 1.D-01 -6.725D-02 9.D-03 9.D-03 2.D-05 3.D-03    7C  0   0
  2 1.D+01 -1.187D+02 0.D+00 -6.699D-02 0.D+00 4.D-04 1.D-04 0.D+00    9C  0   0
  3 1.D+01 -1.187D+02 7.D-04 -6.700D-02 5.D-05 5.D-05 4.D-07 1.D-05   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  6.5849D-04
 Sup-norm of the gradient of the Lagrangian                     =  6.7048D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.1724D-03
 Sup-norm of the gradient of the Lagrangian                     =  4.3169D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.1082D-07
 Sup-norm of the gradient of the Lagrangian                     =  4.7088D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.1082D-07
 Sup-norm of the gradient of the Lagrangian                     =  4.7088D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  8.8818D-16
 Sup-norm of the gradient of the Lagrangian                     =  5.7022D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     33
 Subroutine evalg    (coded=T):     30
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     75 (    37 calls per constraint in average)
 Subroutine evaljac  (coded=T):     34 (    17 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     14
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.1D-03
 Smallest constraints scale factor : 6.8D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.139D+03 0.D+00  1.303D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.659D+01 6.D-02 -9.905D-02 4.D-03 4.D-03 6.D-05 2.D-03    4C  0   0
  2 1.D+01 -8.643D+01 1.D-03 -9.887D-02 9.D-05 9.D-05 6.D-05 4.D-05    5C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6426D+01
 Maximal violation of selected nearly-active constraints        =  1.2565D-03
 Sup-norm of the gradient of the Lagrangian                     =  5.2859D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.7426D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.0049D-06

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.2540D-08
 Sup-norm of the gradient of the Lagrangian                     =  1.5839D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.2540D-08
 Sup-norm of the gradient of the Lagrangian                     =  1.5839D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.1393D-16

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     28
 Subroutine evalg    (coded=T):     20
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     59 (    29 calls per constraint in average)
 Subroutine evaljac  (coded=T):     21 (    10 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      9
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 3.8D-04
 Smallest constraints scale factor : 5.0D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         5.445D+03 0.D+00  2.053D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 2.D+01 -8.649D+01 2.D-02 -3.261D-02 1.D-03 1.D-03 2.D-09 4.D-04    5C  0   0
  2 1.D+01 -8.642D+01 3.D-04 -3.259D-02 1.D-05 1.D-05 5.D-06 5.D-06    6C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  2.6737D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.3280D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.7079D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.1167D-07

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  5.6762D-10
 Sup-norm of the gradient of the Lagrangian                     =  3.3694D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  3.0617D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  3.1619D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     29
 Subroutine evalg    (coded=T):     19
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     61 (    30 calls per constraint in average)
 Subroutine evaljac  (coded=T):     17 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      8
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 3.5D-02
 Smallest constraints scale factor : 5.9D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -7.156D+01 1.D+01 -2.534D+00 6.D+00 6.D+00 1.D+00 7.D+00    0   0   0
  1 1.D+00 -8.706D+01 2.D-01 -3.082D+00 1.D-01 1.D-01 4.D-08 5.D-01    5C  0   0
  2 3.D+01 -8.642D+01 7.D-04 -3.060D+00 4.D-04 4.D-04 4.D-05 2.D-03    7C  0   0
  3 3.D+01 -8.642D+01 2.D-06 -3.060D+00 1.D-06 1.D-06 2.D-06 6.D-06    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  2.3908D-06
 Sup-norm of the gradient of the Lagrangian                     =  5.3227D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.3155D-07
 Sup-norm of the gradient of the Lagrangian                     =  2.8884D-12

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.5075D-14
 Sup-norm of the gradient of the Lagrangian                     =  1.1473D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.7376D-15

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.4211D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     22
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     50 (    25 calls per constraint in average)
 Subroutine evaljac  (coded=T):     31 (    15 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.7D-03
 Smallest constraints scale factor : 1.2D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.036D+02 6.D+00  2.848D-01 6.D+00 6.D+00 1.D+00 6.D+00    0   0   0
  1 5.D-01 -1.355D+02 2.D+00 -3.723D-01 3.D-01 3.D-01 8.D-05 2.D-01    4C  0   0
  2 1.D+01 -1.185D+02 0.D+00 -3.257D-01 0.D+00 7.D-03 2.D-06 0.D+00   12C  0   0
  3 1.D+01 -1.187D+02 4.D-03 -3.263D-01 5.D-04 5.D-04 6.D-09 3.D-04   15C  0   0
  4 1.D+01 -1.187D+02 0.D+00 -3.262D-01 0.D+00 4.D-05 6.D-07 0.D+00   16C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  8.4683D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-07

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-06

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-05

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-04

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-03

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-01

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+00

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+01

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.5785D-06
 Sup-norm of the gradient of the Lagrangian                     =  8.4683D-02

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.9820D-05
 Sup-norm of the gradient of the Lagrangian                     =  6.2965D-02

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.0962D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.6823D-02

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.0624D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.4820D-02

 NEWTON-KKT Iteration                                           =           5
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.3527D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.5894D-02

 NEWTON-KKT Iteration                                           =           6
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.8540D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.9256D-02

 NEWTON-KKT Iteration                                           =           7
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.0253D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.4320D-02

 NEWTON-KKT Iteration                                           =           8
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.6694D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.0649D-02

 NEWTON-KKT Iteration                                           =           9
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.1349D-07
 Sup-norm of the gradient of the Lagrangian                     =  7.9186D-03

 NEWTON-KKT Iteration                                           =          10
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7333D-07
 Sup-norm of the gradient of the Lagrangian                     =  5.8884D-03

 Flag of NEWTON-KKT = Maximum of iterations reached.
  5 1.D+01 -1.187D+02 2.D-05 -3.262D-01 3.D-06 3.D-06 3.D-09 2.D-06   17C  1  10

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.3383D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.1303D-06

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.5711D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.7645D-11

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.6841D-10
 Sup-norm of the gradient of the Lagrangian                     =  1.8353D-13

 Maximal violation of constraints                               =  2.2204D-16

 Maximal violation of Lagrange multipliers non-negativity       =  7.3684D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  8.5265D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     52
 Subroutine evalg    (coded=T):     51
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):    111 (    55 calls per constraint in average)
 Subroutine evaljac  (coded=T):     71 (    35 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     29
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.7D-03
 Smallest constraints scale factor : 7.5D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         6.591D+02 0.D+00  1.137D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.666D+01 8.D-02 -1.494D-01 6.D-03 6.D-03 2.D-08 3.D-03    6C  0   0
  2 1.D+01 -8.643D+01 2.D-03 -1.490D-01 2.D-04 2.D-04 3.D-09 9.D-05    8C  0   0
  3 1.D+01 -8.642D+01 6.D-05 -1.490D-01 4.D-06 4.D-06 1.D-07 2.D-06    9C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  5.6565D-05
 Sup-norm of the gradient of the Lagrangian                     =  6.0519D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  7.8442D-06
 Sup-norm of the gradient of the Lagrangian                     =  9.8288D-10

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  2.5404D-11
 Sup-norm of the gradient of the Lagrangian                     =  7.1280D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  6.4770D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  7.1054D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     32
 Subroutine evalg    (coded=T):     26
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     70 (    35 calls per constraint in average)
 Subroutine evaljac  (coded=T):     22 (    11 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     11
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 3.0D-03
 Smallest constraints scale factor : 1.3D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         7.889D+01 5.D+00  2.374D-01 5.D+00 5.D+00 1.D+00 5.D+00    0   0   0
  1 7.D-01 -1.331D+02 2.D+00 -4.004D-01 3.D-01 3.D-01 3.D-07 1.D-01    5C  0   0
  2 1.D+01 -1.184D+02 0.D+00 -3.564D-01 0.D+00 1.D-02 3.D-06 0.D+00   12C  0   0
  3 1.D+01 -1.187D+02 6.D-03 -3.573D-01 7.D-04 7.D-04 4.D-07 4.D-04   15C  0   0
  4 1.D+01 -1.187D+02 0.D+00 -3.572D-01 0.D+00 6.D-05 1.D-06 0.D+00   16C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.0237D-01

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-07

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-06

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-05

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-04

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-03

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-01

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+00

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+01

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2580D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.0237D-01

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.8961D-05
 Sup-norm of the gradient of the Lagrangian                     =  7.6115D-02

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.6021D-05
 Sup-norm of the gradient of the Lagrangian                     =  5.6606D-02

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  8.8617D-06
 Sup-norm of the gradient of the Lagrangian                     =  4.2099D-02

 NEWTON-KKT Iteration                                           =           5
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.9014D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.1309D-02

 NEWTON-KKT Iteration                                           =           6
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.7109D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.3285D-02

 NEWTON-KKT Iteration                                           =           7
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.4993D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.7317D-02

 NEWTON-KKT Iteration                                           =           8
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  8.2911D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.2878D-02

 NEWTON-KKT Iteration                                           =           9
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.5849D-07
 Sup-norm of the gradient of the Lagrangian                     =  9.5768D-03

 NEWTON-KKT Iteration                                           =          10
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.5352D-07
 Sup-norm of the gradient of the Lagrangian                     =  7.1217D-03

 Flag of NEWTON-KKT = Maximum of iterations reached.
  5 1.D+01 -1.187D+02 4.D-05 -3.572D-01 5.D-06 5.D-06 7.D-09 2.D-06   17C  1  10

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.6478D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.3335D-06

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.1169D-05
 Sup-norm of the gradient of the Lagrangian                     =  6.8544D-11

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.0823D-10
 Sup-norm of the gradient of the Lagrangian                     =  2.8574D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.1472D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.2079D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     54
 Subroutine evalg    (coded=T):     52
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):    117 (    58 calls per constraint in average)
 Subroutine evaljac  (coded=T):     72 (    36 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     29
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.5D-03
 Smallest constraints scale factor : 7.3D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         7.724D+02 0.D+00  1.190D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.664D+01 7.D-02 -1.335D-01 5.D-03 5.D-03 1.D-08 3.D-03    6C  0   0
  2 1.D+01 -8.643D+01 2.D-03 -1.332D-01 1.D-04 1.D-04 2.D-09 7.D-05    8C  0   0
  3 1.D+01 -8.642D+01 5.D-05 -1.332D-01 3.D-06 3.D-06 7.D-08 2.D-06    9C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  4.5896D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.7022D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  6.3647D-06
 Sup-norm of the gradient of the Lagrangian                     =  6.5070D-10

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.6724D-11
 Sup-norm of the gradient of the Lagrangian                     =  5.7835D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  5.2553D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  6.3949D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     33
 Subroutine evalg    (coded=T):     26
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     72 (    36 calls per constraint in average)
 Subroutine evaljac  (coded=T):     22 (    11 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     11
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 4.8D-03
 Smallest constraints scale factor : 1.4D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.266D+01 0.D+00  6.027D-02 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -1.190D+02 9.D-02 -5.667D-01 1.D-02 1.D-02 6.D-05 8.D-03    7C  0   0
  2 1.D+01 -1.187D+02 0.D+00 -5.651D-01 0.D+00 1.D-03 1.D-05 0.D+00    9C  0   0
  3 1.D+01 -1.187D+02 7.D-04 -5.652D-01 1.D-04 1.D-04 3.D-06 6.D-05   10C  0   0
  4 1.D+01 -1.187D+02 0.D+00 -5.652D-01 0.D+00 8.D-06 2.D-08 0.D+00   11C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  3.6446D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  2.2027D-04
 Sup-norm of the gradient of the Lagrangian                     =  4.2326D-06

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.4403D-09
 Sup-norm of the gradient of the Lagrangian                     =  2.4462D-10

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     34
 Subroutine evalg    (coded=T):     31
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     75 (    37 calls per constraint in average)
 Subroutine evaljac  (coded=T):     29 (    14 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     13
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597746409e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 3.1D-04
 Smallest constraints scale factor : 4.8D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         7.055D+03 0.D+00  2.208D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 2.D+01 -8.648D+01 2.D-02 -2.707D-02 9.D-04 9.D-04 3.D-07 3.D-04    5C  0   0
  2 1.D+01 -8.642D+01 2.D-04 -2.705D-02 1.D-05 1.D-05 3.D-06 4.D-06    6C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  2.1559D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.0622D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  2.9897D-05
 Sup-norm of the gradient of the Lagrangian                     =  7.1888D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.6902D-10
 Sup-norm of the gradient of the Lagrangian                     =  2.7168D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.4687D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.4514D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     34
 Subroutine evalg    (coded=T):     21
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     75 (    37 calls per constraint in average)
 Subroutine evaljac  (coded=T):     16 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      8
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.3D-03
 Smallest constraints scale factor : 9.6D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         8.051D+02 2.D-01  1.051D+00 2.D-01 2.D-01 1.D+00 2.D-01    0   0   0
  1 1.D+01 -1.188D+02 4.D-02 -1.551D-01 4.D-03 4.D-03 5.D-03 2.D-03   10C  0   0
  2 1.D+01 -1.187D+02 7.D-03 -1.550D-01 7.D-04 7.D-04 1.D-06 3.D-04   12C  0   0
  3 1.D+01 -1.187D+02 0.D+00 -1.549D-01 0.D+00 4.D-05 1.D-06 0.D+00   13C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  9.7795D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-07

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-06

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-05

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-04

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-03

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-01

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+00

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+01

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.6458D-05
 Sup-norm of the gradient of the Lagrangian                     =  9.7795D-02

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.6431D-05
 Sup-norm of the gradient of the Lagrangian                     =  7.2714D-02

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.4622D-05
 Sup-norm of the gradient of the Lagrangian                     =  5.4078D-02

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  8.0878D-06
 Sup-norm of the gradient of the Lagrangian                     =  4.0218D-02

 NEWTON-KKT Iteration                                           =           5
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.4734D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.9911D-02

 NEWTON-KKT Iteration                                           =           6
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.4741D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.2245D-02

 NEWTON-KKT Iteration                                           =           7
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.3683D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.6543D-02

 NEWTON-KKT Iteration                                           =           8
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.5669D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.2303D-02

 NEWTON-KKT Iteration                                           =           9
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.1844D-07
 Sup-norm of the gradient of the Lagrangian                     =  9.1490D-03

 NEWTON-KKT Iteration                                           =          10
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.3137D-07
 Sup-norm of the gradient of the Lagrangian                     =  6.8036D-03

 Flag of NEWTON-KKT = Maximum of iterations reached.
  4 1.D+01 -1.187D+02 3.D-05 -1.549D-01 3.D-06 3.D-06 3.D-09 1.D-06   14C  1  10

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.7878D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.5040D-06

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.4105D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.0847D-11

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.3594D-10
 Sup-norm of the gradient of the Lagrangian                     =  2.1723D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  8.7216D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  9.7700D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     56
 Subroutine evalg    (coded=T):     48
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):    124 (    62 calls per constraint in average)
 Subroutine evaljac  (coded=T):     63 (    31 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     26
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.0D-03
 Smallest constraints scale factor : 8.9D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.136D+03 3.D+00  1.192D+00 3.D+00 3.D+00 1.D+00 3.D+00    0   0   0
  1 4.D+00 -1.192D+02 1.D-01 -1.251D-01 1.D-02 1.D-02 8.D-06 5.D-03    8C  0   0
  2 1.D+01 -1.187D+02 0.D+00 -1.246D-01 0.D+00 6.D-04 4.D-09 0.D+00   11C  0   0
  3 1.D+01 -1.187D+02 3.D-04 -1.246D-01 3.D-05 3.D-05 9.D-07 1.D-05   12C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  3.4614D-04
 Sup-norm of the gradient of the Lagrangian                     =  8.3580D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.2518D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.4517D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.2233D-08
 Sup-norm of the gradient of the Lagrangian                     =  2.1089D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.2233D-08
 Sup-norm of the gradient of the Lagrangian                     =  2.1089D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.3323D-15
 Sup-norm of the gradient of the Lagrangian                     =  1.2399D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     33
 Subroutine evalg    (coded=T):     31
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     73 (    36 calls per constraint in average)
 Subroutine evaljac  (coded=T):     37 (    18 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     16
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.5D-03
 Smallest constraints scale factor : 7.3D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         8.362D+02 0.D+00  1.257D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.662D+01 7.D-02 -1.303D-01 5.D-03 5.D-03 3.D-05 3.D-03    4C  0   0
  2 1.D+01 -8.643D+01 2.D-03 -1.300D-01 1.D-04 1.D-04 1.D-04 6.D-05    5C  0   0
  3 1.D+01 -8.642D+01 3.D-04 -1.300D-01 2.D-05 2.D-05 3.D-08 1.D-05    6C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  2.8640D-04
 Sup-norm of the gradient of the Lagrangian                     =  2.2873D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.9716D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.8321D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  6.5126D-10
 Sup-norm of the gradient of the Lagrangian                     =  3.6091D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  3.2795D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  3.3396D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     30
 Subroutine evalg    (coded=T):     22
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     64 (    32 calls per constraint in average)
 Subroutine evaljac  (coded=T):     20 (    10 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      8
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.0D-03
 Smallest constraints scale factor : 6.6D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         1.445D+03 0.D+00  1.441D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.657D+01 5.D-02 -8.632D-02 3.D-03 3.D-03 6.D-05 1.D-03    4C  0   0
  2 1.D+01 -8.643D+01 1.D-03 -8.618D-02 7.D-05 7.D-05 4.D-05 3.D-05    5C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6425D+01
 Maximal violation of selected nearly-active constraints        =  1.0404D-03
 Sup-norm of the gradient of the Lagrangian                     =  4.0655D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  1.4428D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.2499D-06

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  8.5958D-09
 Sup-norm of the gradient of the Lagrangian                     =  1.3113D-12

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  1.1916D-12

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.1973D-12

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     26
 Subroutine evalg    (coded=T):     19
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     57 (    28 calls per constraint in average)
 Subroutine evaljac  (coded=T):     16 (     8 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):      7
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185594e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.7D-01
 Smallest constraints scale factor : 2.6D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.183D+02 5.D-01 -2.005D+01 1.D-01 1.D-01 1.D+00 1.D-01    0   0   0
  1 2.D+02 -1.189D+02 5.D-02 -2.015D+01 1.D-02 1.D-02 1.D-07 1.D-02   10C  0   0
  2 2.D+02 -1.187D+02 0.D+00 -2.012D+01 0.D+00 5.D-04 2.D-12 0.D+00   13C  0   0
  3 2.D+02 -1.187D+02 1.D-04 -2.012D+01 3.D-05 3.D-05 1.D-05 3.D-05   14C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  9.7447D-05
 Sup-norm of the gradient of the Lagrangian                     =  7.9335D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7666D-04
 Sup-norm of the gradient of the Lagrangian                     =  8.6197D-10

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.5153D-09
 Sup-norm of the gradient of the Lagrangian                     =  7.0929D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.8477D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.9488D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     46
 Subroutine evalg    (coded=T):     33
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):    103 (    51 calls per constraint in average)
 Subroutine evaljac  (coded=T):     34 (    17 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     16
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 2.0D-02
 Smallest constraints scale factor : 6.0D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.332D+02 1.D+01 -2.630D+00 6.D+00 6.D+00 1.D+00 5.D+00    0   0   0
  1 2.D+00 -1.192D+02 1.D-01 -2.355D+00 8.D-02 8.D-02 7.D-07 2.D-01    7C  0   0
  2 2.D+01 -1.187D+02 0.D+00 -2.345D+00 0.D+00 7.D-04 1.D-04 0.D+00   10C  0   0
  3 2.D+01 -1.187D+02 1.D-05 -2.345D+00 6.D-06 6.D-06 2.D-06 2.D-05   11C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.0530D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.2106D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.4579D-06
 Sup-norm of the gradient of the Lagrangian                     =  9.0246D-11

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7186D-13
 Sup-norm of the gradient of the Lagrangian                     =  1.3008D-14

 Maximal violation of constraints                               =  6.6613D-16

 Maximal violation of Lagrange multipliers non-negativity       =  2.3500D-15

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  1.0658D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     33
 Subroutine evalg    (coded=T):     29
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     76 (    38 calls per constraint in average)
 Subroutine evaljac  (coded=T):     37 (    18 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     13
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.8D-03
 Smallest constraints scale factor : 7.7D-02

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         6.040D+02 0.D+00  1.107D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -8.667D+01 8.D-02 -1.588D-01 6.D-03 6.D-03 9.D-09 4.D-03    5C  0   0
  2 1.D+01 -8.643D+01 2.D-03 -1.584D-01 2.D-04 2.D-04 4.D-09 1.D-04    7C  0   0
  3 1.D+01 -8.642D+01 6.D-05 -1.584D-01 5.D-06 5.D-06 1.D-07 3.D-06    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  6.3472D-05
 Sup-norm of the gradient of the Lagrangian                     =  6.9604D-05

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  8.8020D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.2334D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.1985D-11
 Sup-norm of the gradient of the Lagrangian                     =  7.9982D-14

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  7.2678D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  7.4607D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     29
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     62 (    31 calls per constraint in average)
 Subroutine evaljac  (coded=T):     22 (    11 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 4.5D-03
 Smallest constraints scale factor : 1.4D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0         2.574D+01 0.D+00  1.161D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -1.190D+02 8.D-02 -5.367D-01 1.D-02 1.D-02 5.D-08 7.D-03    8C  0   0
  2 1.D+01 -1.187D+02 0.D+00 -5.352D-01 0.D+00 1.D-03 1.D-05 0.D+00   10C  0   0
  3 1.D+01 -1.187D+02 7.D-04 -5.353D-01 9.D-05 9.D-05 2.D-06 6.D-05   11C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  6.5315D-04
 Sup-norm of the gradient of the Lagrangian                     =  5.5291D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.1781D-03
 Sup-norm of the gradient of the Lagrangian                     =  3.9084D-08

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.1190D-07
 Sup-norm of the gradient of the Lagrangian                     =  4.7317D-12

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.1190D-07
 Sup-norm of the gradient of the Lagrangian                     =  4.7317D-12

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  9.0621D-15

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     31
 Subroutine evalg    (coded=T):     30
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     68 (    34 calls per constraint in average)
 Subroutine evaljac  (coded=T):     29 (    14 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     15
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749956e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 5.8D-03
 Smallest constraints scale factor : 1.5D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -2.086D+01 0.D+00 -1.205D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -1.190D+02 1.D-01 -6.880D-01 1.D-02 1.D-02 2.D-06 1.D-02    6C  0   0
  2 1.D+01 -1.187D+02 0.D+00 -6.859D-01 0.D+00 1.D-03 2.D-05 0.D+00    8C  0   0
  3 1.D+01 -1.187D+02 9.D-04 -6.861D-01 1.D-04 1.D-04 5.D-06 9.D-05    9C  0   0
  4 1.D+01 -1.187D+02 0.D+00 -6.860D-01 0.D+00 1.D-05 4.D-08 0.D+00   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  4.4014D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  3.2141D-04
 Sup-norm of the gradient of the Lagrangian                     =  7.4605D-06

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.0645D-09
 Sup-norm of the gradient of the Lagrangian                     =  6.2887D-10

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     24
 Subroutine evalg    (coded=T):     28
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     51 (    25 calls per constraint in average)
 Subroutine evaljac  (coded=T):     27 (    13 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     12
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597742479e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 8.7D-02
 Smallest constraints scale factor : 1.9D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -2.402D+01 2.D+00 -2.082D+00 4.D-01 4.D-01 1.D+00 4.D-01    0   0   0
  1 2.D+01 -8.730D+01 3.D-01 -7.568D+00 6.D-02 6.D-02 5.D-08 8.D-02    5C  0   0
  2 8.D+01 -8.645D+01 8.D-03 -7.494D+00 2.D-03 2.D-03 3.D-05 2.D-03    7C  0   0
  3 8.D+01 -8.642D+01 2.D-04 -7.492D+00 4.D-05 4.D-05 8.D-05 6.D-05    8C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -8.6423D+01
 Maximal violation of selected nearly-active constraints        =  2.1373D-04
 Sup-norm of the gradient of the Lagrangian                     =  8.7798D-04

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  2.9639D-05
 Sup-norm of the gradient of the Lagrangian                     =  8.6955D-09

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -8.6422D+01
 Maximal violation of selected nearly-active constraints        =  3.6269D-10
 Sup-norm of the gradient of the Lagrangian                     =  2.6934D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  2.4474D-13

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  2.4869D-13

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     21
 Subroutine evalg    (coded=T):     24
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     47 (    23 calls per constraint in average)
 Subroutine evaljac  (coded=T):     26 (    13 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     10
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -8.642220510185595e+01
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 5.9D-03
 Smallest constraints scale factor : 1.5D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -5.824D+01 2.D+00 -3.428D-01 2.D+00 2.D+00 1.D+00 2.D+00    0   0   0
  1 4.D+00 -1.197D+02 3.D-01 -7.048D-01 4.D-02 4.D-02 2.D-05 3.D-02    5C  0   0
  2 1.D+01 -1.186D+02 0.D+00 -6.982D-01 0.D+00 4.D-03 2.D-07 0.D+00    8C  0   0
  3 1.D+01 -1.187D+02 3.D-03 -6.988D-01 4.D-04 4.D-04 4.D-05 3.D-04    9C  0   0
  4 1.D+01 -1.187D+02 0.D+00 -6.987D-01 0.D+00 3.D-05 3.D-07 0.D+00   10C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.4409D-16
 Sup-norm of the gradient of the Lagrangian                     =  6.4160D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-07

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-06

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-05

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-04

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-03

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-01

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+00

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+01

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.6636D-06
 Sup-norm of the gradient of the Lagrangian                     =  6.4160D-02

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.1378D-05
 Sup-norm of the gradient of the Lagrangian                     =  4.7705D-02

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.2919D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.5474D-02

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.4791D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.6378D-02

 NEWTON-KKT Iteration                                           =           5
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.9238D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.9615D-02

 NEWTON-KKT Iteration                                           =           6
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.0637D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.4586D-02

 NEWTON-KKT Iteration                                           =           7
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.8818D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.0846D-02

 NEWTON-KKT Iteration                                           =           8
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.2522D-07
 Sup-norm of the gradient of the Lagrangian                     =  8.0651D-03

 NEWTON-KKT Iteration                                           =           9
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7981D-07
 Sup-norm of the gradient of the Lagrangian                     =  5.9971D-03

 NEWTON-KKT Iteration                                           =          10
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  9.9419D-08
 Sup-norm of the gradient of the Lagrangian                     =  4.4593D-03

 Flag of NEWTON-KKT = Maximum of iterations reached.
  5 1.D+01 -1.187D+02 2.D-05 -6.987D-01 3.D-06 3.D-06 2.D-09 2.D-06   11C  1  10

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7571D-05
 Sup-norm of the gradient of the Lagrangian                     =  2.7002D-07

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.4477D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.4992D-11

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  9.5804D-11
 Sup-norm of the gradient of the Lagrangian                     =  1.3842D-13

 Maximal violation of constraints                               =  1.1102D-15

 Maximal violation of Lagrange multipliers non-negativity       =  5.5576D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  5.5955D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     38
 Subroutine evalg    (coded=T):     44
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     81 (    40 calls per constraint in average)
 Subroutine evaljac  (coded=T):     60 (    30 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     23
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.0D-02
 Smallest constraints scale factor : 1.8D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -8.156D+01 0.D+00 -8.486D-01 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -1.192D+02 1.D-01 -1.240D+00 2.D-02 2.D-02 6.D-07 2.D-02    5C  0   0
  2 1.D+01 -1.187D+02 0.D+00 -1.235D+00 0.D+00 3.D-03 9.D-05 0.D+00    7C  0   0
  3 1.D+01 -1.187D+02 2.D-03 -1.235D+00 3.D-04 3.D-04 2.D-05 2.D-04    8C  0   0
  4 1.D+01 -1.187D+02 0.D+00 -1.235D+00 0.D+00 3.D-05 2.D-07 0.D+00    9C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  5.8717D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-07

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-06

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-05

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-04

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-03

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-02

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D-01

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+00

 Wrong Jacobian inertia. 
 Desired POS =       6 NEG =       4
 Actual  POS =       5 NEG =       5
 epsadd was increased to  1.0000D+01

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  6.6130D-07
 Sup-norm of the gradient of the Lagrangian                     =  5.8717D-02

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  9.5294D-06
 Sup-norm of the gradient of the Lagrangian                     =  4.3658D-02

 NEWTON-KKT Iteration                                           =           3
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  5.2694D-06
 Sup-norm of the gradient of the Lagrangian                     =  3.2464D-02

 NEWTON-KKT Iteration                                           =           4
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.9137D-06
 Sup-norm of the gradient of the Lagrangian                     =  2.4140D-02

 NEWTON-KKT Iteration                                           =           5
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.6111D-06
 Sup-norm of the gradient of the Lagrangian                     =  1.7950D-02

 NEWTON-KKT Iteration                                           =           6
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  8.9081D-07
 Sup-norm of the gradient of the Lagrangian                     =  1.3348D-02

 NEWTON-KKT Iteration                                           =           7
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.9255D-07
 Sup-norm of the gradient of the Lagrangian                     =  9.9252D-03

 NEWTON-KKT Iteration                                           =           8
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  2.7233D-07
 Sup-norm of the gradient of the Lagrangian                     =  7.3803D-03

 NEWTON-KKT Iteration                                           =           9
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.5057D-07
 Sup-norm of the gradient of the Lagrangian                     =  5.4878D-03

 NEWTON-KKT Iteration                                           =          10
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  8.3251D-08
 Sup-norm of the gradient of the Lagrangian                     =  4.0806D-03

 Flag of NEWTON-KKT = Maximum of iterations reached.
  5 1.D+01 -1.187D+02 2.D-05 -1.235D+00 3.D-06 3.D-06 2.D-09 2.D-06   10C  1  10

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.5819D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.7642D-07

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  3.1053D-05
 Sup-norm of the gradient of the Lagrangian                     =  1.2053D-11

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  7.7721D-11
 Sup-norm of the gradient of the Lagrangian                     =  1.2468D-13

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  5.0056D-14

 GENCAN is being called to find the right multipliers.

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00
 Sup-norm of the gradient of the Lagrangian                     =  5.0182D-14

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     39
 Subroutine evalg    (coded=T):     43
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     82 (    41 calls per constraint in average)
 Subroutine evaljac  (coded=T):     54 (    27 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     22
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597749957e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 1.3D-02
 Smallest constraints scale factor : 1.8D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -9.511D+01 0.D+00 -1.223D+00 0.D+00 0.D+00 1.D+00 0.D+00    0   0   0
  1 1.D+01 -1.191D+02 1.D-01 -1.533D+00 2.D-02 2.D-02 3.D-08 2.D-02    4C  0   0
  2 2.D+01 -1.187D+02 0.D+00 -1.526D+00 0.D+00 2.D-03 6.D-11 0.D+00    7C  0   0
  3 2.D+01 -1.187D+02 1.D-03 -1.527D+00 2.D-04 2.D-04 2.D-05 2.D-04    8C  0   0
  4 2.D+01 -1.187D+02 0.D+00 -1.527D+00 0.D+00 2.D-05 1.D-07 0.D+00    9C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  4.7191D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1871D+02
 Maximal violation of selected nearly-active constraints        =  3.6983D-04
 Sup-norm of the gradient of the Lagrangian                     =  9.2056D-06

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.0506D-09
 Sup-norm of the gradient of the Lagrangian                     =  8.9210D-10

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.01

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     22
 Subroutine evalg    (coded=T):     27
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     47 (    23 calls per constraint in average)
 Subroutine evaljac  (coded=T):     28 (    14 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     11
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597740107e+02
ALGENCAN: 
 ==============================================================================
 This is ALGENCAN 2.4.0.
 ALGENCAN, an augmented Lagrangian method for nonlinear programming, is part of
 the TANGO Project: Trustable Algorithms for Nonlinear General Optimization.
 See http://www.ime.usp.br/~egbirgin/tango/ for details.
 ==============================================================================

 The optional specification file algencan.dat was not found in the current
 directory (this is not a problem nor an error). The default values for the
 ALGENCAN parameters will be used.

 ALGENCAN PARAMETERS:
 firstde  =                    T
 truehl   =                    T
 hptype   =               TRUEHP
 innslvr  =                   TR
 sclsys   =                    T
 skipacc  =                    F
 lsssub   =                 MA57
 sclsub   =                 MA57
 rmfixv   =                    T
 slacks   =                    F
 scale    =                    T
 epsfeas  =           1.0000D-08
 epsopt   =           1.0000D-08
 efacc    =           1.0000D-04
 eoacc    =           1.0000D-04
 iprint   =                   10
 ncomp    =                    6

 Number of variables               :       2
 Number of equality constraints    :       0
 Number of inequality constraints  :       2
 Number of bound constraints       :       4

 Number of removed fixed variables :       0

 Objective function scale factor   : 6.0D-02
 Smallest constraints scale factor : 2.5D-01

 Entry to ALGENCAN.
 Number of variables  :       2
 Number of constraints:       2

out penalt  objective infeas  scaled    scaled infeas norm   |Grad| inner Newton
ite         function  ibilty  obj-funct infeas +compl graLag infeas totit forKKT
  0        -1.875D+02 8.D+00 -1.116D+01 3.D+00 3.D+00 1.D+00 4.D+00    0   0   0
  1 1.D+01 -1.198D+02 3.D-01 -7.131D+00 7.D-02 7.D-02 1.D-08 8.D-02    6C  0   0
  2 7.D+01 -1.187D+02 0.D+00 -7.063D+00 0.D+00 3.D-03 8.D-06 0.D+00   13C  0   0
  3 7.D+01 -1.187D+02 7.D-04 -7.066D+00 2.D-04 2.D-04 2.D-07 2.D-04   15C  0   0
  4 7.D+01 -1.187D+02 0.D+00 -7.066D+00 0.D+00 8.D-06 4.D-07 0.D+00   16C  0   0

 NEWTON-KKT scheme in action!

 NEWTON-KKT Iteration                                           =           0
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.7764D-15
 Sup-norm of the gradient of the Lagrangian                     =  2.7433D-02

 NEWTON-KKT Iteration                                           =           1
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  1.2573D-04
 Sup-norm of the gradient of the Lagrangian                     =  1.8237D-06

 NEWTON-KKT Iteration                                           =           2
 Objective function value                                       = -1.1870D+02
 Maximal violation of selected nearly-active constraints        =  4.6219D-10
 Sup-norm of the gradient of the Lagrangian                     =  5.9713D-11

 Maximal violation of constraints                               =  0.0000D+00

 Maximal violation of Lagrange multipliers non-negativity       =  0.0000D+00

 Flag of NEWTON-KKT = KKT system solved!

 Flag of ALGENCAN: Solution was found.

 Total CPU time in seconds:     0.00

 User-provided subroutines calls counters: 

 Subroutine evalf    (coded=T):     39
 Subroutine evalg    (coded=T):     35
 Subroutine evalh    (coded=F):      0
 Subroutine evalc    (coded=T):     85 (    42 calls per constraint in average)
 Subroutine evaljac  (coded=T):     42 (    21 calls per constraint in average)
 Subroutine evalhc   (coded=F):      0 (     0 calls per constraint in average)
 Subroutine evalfc   (coded=F):      0
 Subroutine evalgjac (coded=F):      0
 Subroutine evalhl   (coded=T):     18
 Subroutine evalhlp  (coded=F):      0

LOCAL f = -1.187048597748797e+02
