// -------------------------------------------------------------- -*- C++ -*-
// File: examples/src/iloqpex1.cpp
// Version 8.1
// --------------------------------------------------------------------------
// Copyright (C) 1999-2002 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
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