ipopt_solve_get_started.cpp

View page source

Nonlinear Programming Using CppAD and Ipopt: Example and Test

Purpose

This example program demonstrates how to use ipopt_solve to solve the example problem in the Ipopt documentation; i.e., the problem

\[\begin{split}\begin{array}{lc} {\rm minimize \; } & x_1 * x_4 * (x_1 + x_2 + x_3) + x_3 \\ {\rm subject \; to \; } & x_1 * x_2 * x_3 * x_4 \geq 25 \\ & x_1^2 + x_2^2 + x_3^2 + x_4^2 = 40 \\ & 1 \leq x_1, x_2, x_3, x_4 \leq 5 \end{array}\end{split}\]

Configuration Requirement

This example will be compiled and tested provided include_ipopt is on the cmake command line.

# include <cppad/ipopt/solve.hpp>

namespace {
   using CppAD::AD;

   class FG_eval {
   public:
      typedef CPPAD_TESTVECTOR( AD<double> ) ADvector;
      void operator()(ADvector& fg, const ADvector& x)
      {  assert( fg.size() == 3 );
         assert( x.size()  == 4 );

         // Fortran style indexing
         AD<double> x1 = x[0];
         AD<double> x2 = x[1];
         AD<double> x3 = x[2];
         AD<double> x4 = x[3];
         // f(x)
         fg[0] = x1 * x4 * (x1 + x2 + x3) + x3;
         // g_1 (x)
         fg[1] = x1 * x2 * x3 * x4;
         // g_2 (x)
         fg[2] = x1 * x1 + x2 * x2 + x3 * x3 + x4 * x4;
         //
         return;
      }
   };
}

bool get_started(void)
{  bool ok = true;
   size_t i;
   typedef CPPAD_TESTVECTOR( double ) Dvector;

   // number of independent variables (domain dimension for f and g)
   size_t nx = 4;
   // number of constraints (range dimension for g)
   size_t ng = 2;
   // initial value of the independent variables
   Dvector xi(nx);
   xi[0] = 1.0;
   xi[1] = 5.0;
   xi[2] = 5.0;
   xi[3] = 1.0;
   // lower and upper limits for x
   Dvector xl(nx), xu(nx);
   for(i = 0; i < nx; i++)
   {  xl[i] = 1.0;
      xu[i] = 5.0;
   }
   // lower and upper limits for g
   Dvector gl(ng), gu(ng);
   gl[0] = 25.0;     gu[0] = 1.0e19;
   gl[1] = 40.0;     gu[1] = 40.0;

   // object that computes objective and constraints
   FG_eval fg_eval;

   // options
   std::string options;
   // turn off any printing
   options += "Integer print_level  0\n";
   options += "String  sb           yes\n";
   // maximum number of iterations
   options += "Integer max_iter     10\n";
   // approximate accuracy in first order necessary conditions;
   // see Mathematical Programming, Volume 106, Number 1,
   // Pages 25-57, Equation (6)
   options += "Numeric tol          1e-6\n";
   // derivative testing
   options += "String  derivative_test            second-order\n";
   // maximum amount of random pertubation; e.g.,
   // when evaluation finite diff
   options += "Numeric point_perturbation_radius  0.\n";

   // place to return solution
   CppAD::ipopt::solve_result<Dvector> solution;

   // solve the problem
   CppAD::ipopt::solve<Dvector, FG_eval>(
      options, xi, xl, xu, gl, gu, fg_eval, solution
   );
   //
   // Check some of the solution values
   //
   ok &= solution.status == CppAD::ipopt::solve_result<Dvector>::success;
   //
   double check_x[]  = { 1.000000, 4.743000, 3.82115, 1.379408 };
   double check_zl[] = { 1.087871, 0.,       0.,      0.       };
   double check_zu[] = { 0.,       0.,       0.,      0.       };
   double rel_tol    = 1e-6;  // relative tolerance
   double abs_tol    = 1e-6;  // absolute tolerance
   for(i = 0; i < nx; i++)
   {  ok &= CppAD::NearEqual(
         check_x[i],  solution.x[i],   rel_tol, abs_tol
      );
      ok &= CppAD::NearEqual(
         check_zl[i], solution.zl[i], rel_tol, abs_tol
      );
      ok &= CppAD::NearEqual(
         check_zu[i], solution.zu[i], rel_tol, abs_tol
      );
   }

   return ok;
}