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(:title Download IPOPT for AMPL:) (:keywords nonlinear programming, IPOPT, interior point solver, AMPL, mathematical programming language:) (:description Download the AMPL IPOPT for Windows OS (ipopt.exe).:)

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## APMonitor Software Trial

IPOPT is a solver supported in the APMonitor optimization suite. A trial version of APMonitor can be accessed through the following on-line interface.

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## APMonitor

IPOPT is a solver supported in the APMonitor Modeling Language. An online version of APMonitor can be accessed through the following interface:

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APMonitor Online Solution Engine

## Results from make_f95.bat

(:table border=1 width=50% align=left bgcolor=#EEEEEE cellspacing=0:) (:cellnr:) (:html:) <SMALL> <pre>

 C:\ipopt>cd hs071_f95

C:\ipopt\hs071_f95>set PATH=..\g95\bin

C:\ipopt\hs071_f95>set PATH=..\g95\lib\gcc-lib\i686-pc-mingw32\4.0.3;..\g95\bin

C:\ipopt\hs071_f95>set LIBRARY_PATH=..\g95\lib;

C:\ipopt\hs071_f95>set LIBRARY_PATH=..\g95\lib\gcc-lib\i686-pc-mingw32\4.0.3;..\g95\lib;

C:\ipopt\hs071_f95>g95 -c -ftrace=full -fbounds-check hs071.f90

C:\ipopt\hs071_f95>g95 -O3 -o ipopt *.o ../lib/libipopt_mumps_v3.5.4.a ../lib/libstdc++.a

C:\ipopt\hs071_f95>ipopt.exe

******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Common Public License (CPL).
******************************************************************************

NOTE: You are using Ipopt by default with the MUMPS linear solver.
Other linear solvers might be more efficient (see Ipopt documentation).

This is Ipopt version 3.5.4, running with linear solver mumps.

Number of nonzeros in equality constraint Jacobian...:        4
Number of nonzeros in inequality constraint Jacobian.:        4
Number of nonzeros in Lagrangian Hessian.............:       10

Total number of variables............................:        4
variables with only lower bounds:        0
variables with lower and upper bounds:        4
variables with only upper bounds:        0
Total number of equality constraints.................:        1
Total number of inequality constraints...............:        1
inequality constraints with only lower bounds:        1
inequality constraints with lower and upper bounds:        0
inequality constraints with only upper bounds:        0

iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
0 1.6109693e+001 1.12e+001 5.28e-001   0.0 0.00e+000    -  0.00e+000 0.00e+000   0
1 1.7410406e+001 8.38e-001 2.25e+001  -0.3 7.97e-001    -  3.19e-001 1.00e+000f  1
2 1.8001613e+001 1.06e-002 4.96e+000  -0.3 5.60e-002   2.0 9.97e-001 1.00e+000h  1
3 1.7199482e+001 9.04e-002 4.24e-001  -1.0 9.91e-001    -  9.98e-001 1.00e+000f  1
4 1.6940955e+001 2.09e-001 4.58e-002  -1.4 2.88e-001    -  9.66e-001 1.00e+000h  1
5 1.7003411e+001 2.29e-002 8.42e-003  -2.9 7.03e-002    -  9.68e-001 1.00e+000h  1
6 1.7013974e+001 2.59e-004 8.65e-005  -4.5 6.22e-003    -  1.00e+000 1.00e+000h  1
7 1.7014017e+001 2.88e-007 2.18e-007 -10.3 1.43e-004    -  9.99e-001 1.00e+000h  1
8 1.7014017e+001 1.07e-013 1.99e-014 -11.0 1.04e-007    -  1.00e+000 1.00e+000h  1

Number of Iterations....: 8

(scaled)                 (unscaled)
Objective...............:  1.7014017140224137e+001   1.7014017140224137e+001
Dual infeasibility......:  1.9949516030427708e-014   1.9949516030427708e-014
Constraint violation....:  2.1316282072803006e-014   2.1316282072803006e-014
Complementarity.........:  1.0023967333275255e-011   1.0023967333275255e-011
Overall NLP error.......:  1.0023967333275255e-011   1.0023967333275255e-011

Number of objective function evaluations             = 9
Number of objective gradient evaluations             = 9
Number of equality constraint evaluations            = 9
Number of inequality constraint evaluations          = 9
Number of equality constraint Jacobian evaluations   = 9
Number of inequality constraint Jacobian evaluations = 9
Number of Lagrangian Hessian evaluations             = 8
Total CPU secs in IPOPT (w/o function evaluations)   =      0.047
Total CPU secs in NLP function evaluations           =     -0.000

EXIT: Optimal Solution Found.

The solution was found.

The final value of the objective function is  17.014017140224137

The optimal values of X are:

X  ( 1 ) =  1.
X  ( 2 ) =  4.74299964357774
X  ( 3 ) =  3.8211499789476258
X  ( 4 ) =  1.3794082932154592

The multipliers for the lower bounds are:

Z_L( 1 ) =  1.0878712102394714
Z_L( 2 ) =  2.6716076302477845E-12
Z_L( 3 ) =  3.5449114894908155E-12
Z_L( 4 ) =  2.6354492381641954E-11

The multipliers for the upper bounds are:

Z_U( 1 ) =  2.4999426998435727E-12
Z_U( 2 ) =  3.896984124618816E-11
Z_U( 3 ) =  8.482035758568486E-12
Z_U( 4 ) =  2.7621630987933214E-12

The multipliers for the equality constraints are:

LAM( 1 ) =  -0.5522936595011606
LAM( 2 ) =  0.16146856417868571

C:\ipopt\hs071_f95>pause
Press any key to continue . . .


</pre> </SMALL> (:htmlend:) (:tableend:)

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APMonitor Online Solutions with IPOPT and MUMPS

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## IPOPT Library (libipopt.a) and g95 Compiler

This archive contains an IPOPT compiled library for use on Windows OS computers. The zip archive also contains the g95 (v0.9) Fortran compiler, compatible with gcc (v4.0.3). The packages contain all files necessary to run the IPOPT solver on the Windows platform.

Once the package has been downloaded and unzipped, two example problems can be tested with make_f77.bat and make_f95.bat batch files. These batch files compile the hs071 Fortran examples and link them to the IPOPT library to form ipopt.exe. The batch file then runs the ipopt.exe program.

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## IPOPT Libraries

The COIN-OR web-site contains binaries of IPOPT for a variety of operating systems and compilers (link).

Once the package has been downloaded and unzipped, an example problems can be compiled and linked to the IPOPT library. The following contains a Fortran90 interface to IPOPT.

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APMonitor Quick Start Guide

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APMonitor Quick Start Guide

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APMonitor Online Solution Engine

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APMonitor Online Solution Engine

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This archive contains an IPOPT AMPL executable for use on Windows OS computers. The zip archive contains ipopt.exe. The ipopt.exe reads a file that is produced by ampl.exe to solve the nonlinear programming problem.

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This archive contains an IPOPT AMPL executable for use on Windows OS computers. The zip archive contains ipopt.exe. The ipopt.exe reads a file that is produced by ampl.exe to solve nonlinear programming problems.

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This page contains an IPOPT compiled library and AMPL executable for use on Windows OS computers. The zip archive also contains the g95 (v0.9) Fortran compiler, compatible with gcc (v4.0.3). The packages contain all files necessary to run the IPOPT solver on the Windows platform.

This archive contains an IPOPT AMPL executable for use on Windows OS computers. The zip archive contains ipopt.exe. The ipopt.exe reads a file that is produced by ampl.exe to solve the nonlinear programming problem.

This archive contains an IPOPT compiled library for use on Windows OS computers. The zip archive also contains the g95 (v0.9) Fortran compiler, compatible with gcc (v4.0.3). The packages contain all files necessary to run the IPOPT solver on the Windows platform.

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## IPOPT for AMPL

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## IPOPT Library (libipopt.a) and g95 Compiler

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This IPOPT executable uses http://mumps.enseeiht.fr/?, a parallel sparse direct solver.

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This IPOPT executable uses MUMPS, a parallel sparse direct solver.

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This page contains an IPOPT compiled library for use on Windows OS computers. The zip archive also contains the g95 (v0.9) Fortran compiler, compatible with gcc (v4.0.3). The package contains all files necessary to run the IPOPT solver on the Windows platform.

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This page contains an IPOPT compiled library and AMPL executable for use on Windows OS computers. The zip archive also contains the g95 (v0.9) Fortran compiler, compatible with gcc (v4.0.3). The packages contain all files necessary to run the IPOPT solver on the Windows platform.

### IPOPT for AMPL

AMPL is a comprehensive and powerful algebraic modeling language for linear and nonlinear optimization problems, in discrete or continuous variables. Developed at Bell Laboratories, AMPL lets you use common notation and familiar concepts to formulate optimization models and examine solutions, while the computer manages communication with an appropriate solver. AMPL's flexibility and convenience render it ideal for rapid prototyping and model development, while its speed and control options make it an especially efficient choice for repeated production runs. A student version of AMPL can be obtained at the AMPL website.

This IPOPT executable uses http://mumps.enseeiht.fr/?, a parallel sparse direct solver.

### IPOPT Library (libipopt.a) and g95 Compiler

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APMonitor Quick Start Guide

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APMonitor Quick Start Guide

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APMonitor Optimization with IPOPT

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APMonitor Quick Start Guide APMonitor Online Solution Engine

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IPOPT or 'Interior Point Optimizer' is a large-scale nonlinear programming (NLP) solver. It is part of the the COIN-OR open-source effort for the development of solvers for problems in the field of operations research. There are various repositories that contain binaries of IPOPT for a variety of operating systems and compilers (see link).

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IPOPT or 'Interior Point Optimizer' is a large-scale nonlinear programming (NLP) solver. It is part of the the COIN-OR open-source effort for the development of solvers for problems in the field of operations research. The COIN-OR web-site also contains binaries of IPOPT for a variety of operating systems and compilers (link).

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IPOPT or 'Interior Point Optimizer' is a large-scale nonlinear programming (NLP) solver. It is part of the the COIN-OR open-source effort for the development of solvers for problems in the field of operations research. There are various repositories that contain binaries of IPOPT for a variety of operating systems and compilers.

to:

IPOPT or 'Interior Point Optimizer' is a large-scale nonlinear programming (NLP) solver. It is part of the the COIN-OR open-source effort for the development of solvers for problems in the field of operations research. There are various repositories that contain binaries of IPOPT for a variety of operating systems and compilers (see link).

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(:tableend:)

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(:tableend:)

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This page contains an IPOPT compiled library for use on Windows (32-bit) computers. The zip archive also contains the g95 (v0.9) Fortran compiler, compatible with gcc (v4.0.3). The package contains all files necessary to run the IPOPT solver on the Windows platform.

to:

This page contains an IPOPT compiled library for use on Windows OS computers. The zip archive also contains the g95 (v0.9) Fortran compiler, compatible with gcc (v4.0.3). The package contains all files necessary to run the IPOPT solver on the Windows platform.

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(:html:)<b>Hello world</b>(:htmlend:)

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(:html:)<b>Hello world</b>(:htmlend:)

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(:html:)

<pre>

</pre>

(:htmlend:)

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'--

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<SMALL>

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--'

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</SMALL>

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'--

--'

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## APMonitor Software Trial

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IPOPT is a solver supported in the APMonitor optimization suite. A trial version of APMonitor can be accessed through the following link.

APMonitor Optimization Suite

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IPOPT is a solver supported in the APMonitor optimization suite. A trial version of APMonitor can be accessed through the following on-line interface.

APMonitor Optimization with IPOPT

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APMonitor Optimization Suite

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APMonitor Optimization Suite

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IPOPT is a solver supported in the APMonitor optimization suite. A trial version of APMonitor can be accessed through the following link.

APMonitor Optimization Suite

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This page contains an IPOPT compiled library for use on Windows (32-bit) computers. The zip archive also contains the g95 (v0.9) Fortran compiler, compatible with gcc (v4.0.3). The package contains all files necessary to run the IPOPT solver.

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This page contains an IPOPT compiled library for use on Windows (32-bit) computers. The zip archive also contains the g95 (v0.9) Fortran compiler, compatible with gcc (v4.0.3). The package contains all files necessary to run the IPOPT solver on the Windows platform.

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## Results from make_f95.bat

(:table border=1 width=50% align=left bgcolor=#EEEEEE cellspacing=0:) (:cellnr:)

 C:\ipopt>cd hs071_f95

C:\ipopt\hs071_f95>set PATH=..\g95\bin

C:\ipopt\hs071_f95>set PATH=..\g95\lib\gcc-lib\i686-pc-mingw32\4.0.3;..\g95\bin

C:\ipopt\hs071_f95>set LIBRARY_PATH=..\g95\lib;

C:\ipopt\hs071_f95>set LIBRARY_PATH=..\g95\lib\gcc-lib\i686-pc-mingw32\4.0.3;..\g95\lib;

C:\ipopt\hs071_f95>g95 -c -ftrace=full -fbounds-check hs071.f90

C:\ipopt\hs071_f95>g95 -O3 -o ipopt *.o ../lib/libipopt_mumps_v3.5.4.a ../lib/libstdc++.a

C:\ipopt\hs071_f95>ipopt.exe

******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Common Public License (CPL).
******************************************************************************

NOTE: You are using Ipopt by default with the MUMPS linear solver.
Other linear solvers might be more efficient (see Ipopt documentation).

This is Ipopt version 3.5.4, running with linear solver mumps.

Number of nonzeros in equality constraint Jacobian...:        4
Number of nonzeros in inequality constraint Jacobian.:        4
Number of nonzeros in Lagrangian Hessian.............:       10

Total number of variables............................:        4
variables with only lower bounds:        0
variables with lower and upper bounds:        4
variables with only upper bounds:        0
Total number of equality constraints.................:        1
Total number of inequality constraints...............:        1
inequality constraints with only lower bounds:        1
inequality constraints with lower and upper bounds:        0
inequality constraints with only upper bounds:        0

iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
0 1.6109693e+001 1.12e+001 5.28e-001   0.0 0.00e+000    -  0.00e+000 0.00e+000   0
1 1.7410406e+001 8.38e-001 2.25e+001  -0.3 7.97e-001    -  3.19e-001 1.00e+000f  1
2 1.8001613e+001 1.06e-002 4.96e+000  -0.3 5.60e-002   2.0 9.97e-001 1.00e+000h  1
3 1.7199482e+001 9.04e-002 4.24e-001  -1.0 9.91e-001    -  9.98e-001 1.00e+000f  1
4 1.6940955e+001 2.09e-001 4.58e-002  -1.4 2.88e-001    -  9.66e-001 1.00e+000h  1
5 1.7003411e+001 2.29e-002 8.42e-003  -2.9 7.03e-002    -  9.68e-001 1.00e+000h  1
6 1.7013974e+001 2.59e-004 8.65e-005  -4.5 6.22e-003    -  1.00e+000 1.00e+000h  1
7 1.7014017e+001 2.88e-007 2.18e-007 -10.3 1.43e-004    -  9.99e-001 1.00e+000h  1
8 1.7014017e+001 1.07e-013 1.99e-014 -11.0 1.04e-007    -  1.00e+000 1.00e+000h  1

Number of Iterations....: 8

(scaled)                 (unscaled)
Objective...............:  1.7014017140224137e+001   1.7014017140224137e+001
Dual infeasibility......:  1.9949516030427708e-014   1.9949516030427708e-014
Constraint violation....:  2.1316282072803006e-014   2.1316282072803006e-014
Complementarity.........:  1.0023967333275255e-011   1.0023967333275255e-011
Overall NLP error.......:  1.0023967333275255e-011   1.0023967333275255e-011

Number of objective function evaluations             = 9
Number of objective gradient evaluations             = 9
Number of equality constraint evaluations            = 9
Number of inequality constraint evaluations          = 9
Number of equality constraint Jacobian evaluations   = 9
Number of inequality constraint Jacobian evaluations = 9
Number of Lagrangian Hessian evaluations             = 8
Total CPU secs in IPOPT (w/o function evaluations)   =      0.047
Total CPU secs in NLP function evaluations           =     -0.000

EXIT: Optimal Solution Found.

The solution was found.

The final value of the objective function is  17.014017140224137

The optimal values of X are:

X  ( 1 ) =  1.
X  ( 2 ) =  4.74299964357774
X  ( 3 ) =  3.8211499789476258
X  ( 4 ) =  1.3794082932154592

The multipliers for the lower bounds are:

Z_L( 1 ) =  1.0878712102394714
Z_L( 2 ) =  2.6716076302477845E-12
Z_L( 3 ) =  3.5449114894908155E-12
Z_L( 4 ) =  2.6354492381641954E-11

The multipliers for the upper bounds are:

Z_U( 1 ) =  2.4999426998435727E-12
Z_U( 2 ) =  3.896984124618816E-11
Z_U( 3 ) =  8.482035758568486E-12
Z_U( 4 ) =  2.7621630987933214E-12

The multipliers for the equality constraints are:

LAM( 1 ) =  -0.5522936595011606
LAM( 2 ) =  0.16146856417868571

C:\ipopt\hs071_f95>pause
Press any key to continue . . .


(:tableend:)

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## IPOPT

IPOPT or 'Interior Point Optimizer' is a large-scale nonlinear programming (NLP) solver. It is part of the the COIN-OR open-source effort for the development of solvers for problems in the field of operations research. There are various repositories that contain binaries of IPOPT for a variety of operating systems and compilers.

This page contains an IPOPT compiled library for use on Windows (32-bit) computers. The zip archive also contains the g95 (v0.9) Fortran compiler, compatible with gcc (v4.0.3). The package contains all files necessary to run the IPOPT solver.

Once the package has been downloaded and unzipped, two example problems can be tested with make_f77.bat and make_f95.bat batch files. These batch files compile the hs071 Fortran examples and link them to the IPOPT library to form ipopt.exe. The batch file then runs the ipopt.exe program.