This example is almost identical to ilolpex1.cpp with only function populatebyrow to create the model. Also, this function differs only in the creation of the objective from its ilolpex1.cpp counterpart. Here the objective function is created and added to the model like this:

Complete Program: iloqpex1.cpp

The complete program, iloqpex1.cpp, appears here and online in the standard distribution.

// -------------------------------------------------------------- -*- C++ -*-
// File: examples/src/iloqpex1.cpp
// Version 9.0    
// --------------------------------------------------------------------------
//  Copyright (C) 1999-2003 by ILOG.
//  All Rights Reserved.
//  Permission is expressly granted to use this example in the
//  course of developing applications that use ILOG products.
// --------------------------------------------------------------------------
//
// iloqpex1.cpp - Entering and optimizing a quadratic problem.

#include <ilcplex/ilocplex.h>
ILOSTLBEGIN

static void
   populatebyrow     (IloModel model, IloNumVarArray var, IloRangeArray con);

int
main (int argc, char **argv)
{
   IloEnv   env;
   try {
      IloModel model(env);
      IloNumVarArray var(env);
      IloRangeArray con(env);

      populatebyrow (model, var, con);

      IloCplex cplex(model);

      // Optimize the problem and obtain solution.
      if ( !cplex.solve() ) {
         env.error() << "Failed to optimize LP" << endl;
         throw(-1);
      }

      IloNumArray vals(env);
      env.out() << "Solution status = " << cplex.getStatus() << endl;
      env.out() << "Solution value  = " << cplex.getObjValue() << endl;
      cplex.getValues(vals, var);
      env.out() << "Values        = " << vals << endl;
      cplex.getSlacks(vals, con);
      env.out() << "Slacks        = " << vals << endl;
      cplex.getDuals(vals, con);
      env.out() << "Duals         = " << vals << endl;
      cplex.getReducedCosts(vals, var);
      env.out() << "Reduced Costs = " << vals << endl;

      cplex.exportModel("qpex1.lp");
   }
   catch (IloException& e) {
      cerr << "Concert exception caught: " << e << endl;
   }
   catch (...) {
      cerr << "Unknown exception caught" << endl;
   }

   env.end();

   return 0;
}  // END main


// To populate by row, we first create the variables, and then use them to
// create the range constraints and objective.  The model we create is:
//
//    Maximize
//     obj: x1 + 2 x2 + 3 x3
//            - 0.5 ( 33*x1*x1 + 22*x2*x2 + 11*x3*x3
//                             - 12*x1*x2 - 23*x2*x3 )
//    Subject To
//     c1: - x1 + x2 + x3 <= 20
//     c2: x1 - 3 x2 + x3 <= 30
//    Bounds
//     0 <= x1 <= 40
//    End

static void
populatebyrow (IloModel model, IloNumVarArray x, IloRangeArray c)
{
   IloEnv env = model.getEnv();

   x.add(IloNumVar(env, 0.0, 40.0));
   x.add(IloNumVar(env));
   x.add(IloNumVar(env));
   model.add(IloMaximize(env, x[0] + 2 * x[1] + 3 * x[2]
                            - 0.5 * (33*x[0]*x[0] + 22*x[1]*x[1] +
                                     11*x[2]*x[2] - 12*x[0]*x[1] -
                                     23*x[1]*x[2]                 ) ));

   c.add( - x[0] +     x[1] + x[2] <= 20);
   c.add(   x[0] - 3 * x[1] + x[2] <= 30);
   model.add(c);
}  // END populatebyrow