Compiling and Testing the Example

Our next task is to compile and test the code. If you are familiar with the compiler and linker used on your system, you can build the code, telling the linker about the IPOPT library libipopt.so (or libipopt.a), as well as other necessary libraries, as listed in the ipopt_addlibs_cpp.txt and ipopt_addlibs_f.txt files. If you are using Linux/UNIX, then a sample makefile exists already that was created by configure. Copy Ipopt/examples/hs071_cpp/Makefile into your MyExample directory. This makefile was created for the hs071_cpp code, but it can be easily modified for your example problem. Edit the file, making the following changes,

• change the EXE variable
  EXE = my_example
• change the OBJS variable
  OBJS = HS071_NLP.o MyExample.o

and the problem should compile easily with,
$ make
Now run the executable,
$ ./my_example
and you should see output resembling the following,

******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
 Ipopt is released as open source code under the Common Public License (CPL).
         For more information visit http://projects.coin-or.org/Ipopt
******************************************************************************

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+01 1.12e+01 5.28e-01   0.0 0.00e+00    -  0.00e+00 0.00e+00   0
   1  1.7410406e+01 8.38e-01 2.25e+01  -0.3 7.97e-01    -  3.19e-01 1.00e+00f  1
   2  1.8001613e+01 1.06e-02 4.96e+00  -0.3 5.60e-02   2.0 9.97e-01 1.00e+00h  1
   3  1.7199482e+01 9.04e-02 4.24e-01  -1.0 9.91e-01    -  9.98e-01 1.00e+00f  1
   4  1.6940955e+01 2.09e-01 4.58e-02  -1.4 2.88e-01    -  9.66e-01 1.00e+00h  1
   5  1.7003411e+01 2.29e-02 8.42e-03  -2.9 7.03e-02    -  9.68e-01 1.00e+00h  1
   6  1.7013974e+01 2.59e-04 8.65e-05  -4.5 6.22e-03    -  1.00e+00 1.00e+00h  1
   7  1.7014017e+01 2.26e-07 5.71e-08  -8.0 1.43e-04    -  1.00e-00 1.00e+00h  1
   8  1.7014017e+01 4.62e-14 9.09e-14  -8.0 6.95e-08    -  1.00e+00 1.00e+00h  1

Number of Iterations....: 8

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)   =      0.220
Total CPU secs in NLP function evaluations           =      0.000

EXIT: Optimal Solution Found.


Solution of the primal variables, x
x[0] = 1.000000e+00
x[1] = 4.743000e+00
x[2] = 3.821150e+00
x[3] = 1.379408e+00


Solution of the bound multipliers, z_L and z_U
z_L[0] = 1.087871e+00
z_L[1] = 2.428776e-09
z_L[2] = 3.222413e-09
z_L[3] = 2.396076e-08
z_U[0] = 2.272727e-09
z_U[1] = 3.537314e-08
z_U[2] = 7.711676e-09
z_U[3] = 2.510890e-09


Objective value
f(x*) = 1.701402e+01


*** The problem solved!

This completes the basic C++ tutorial, but see Section 6 which explains the standard console output of IPOPT and Section 5 for information about the use of options to customize the behavior of IPOPT.

The Ipopt/examples/ScalableProblems directory contains other NLP problems coded in C++.