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CPLEXsolver.cpp
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#include <iostream>
#include "CPLEXsolver.h"
CPLEXsolver::~CPLEXsolver()
{
/** Destroy CPLEX environment */
_IloEnv.end();
_IloInitialized = false;
}
CPLEXsolver::CPLEXsolver(const int numVariable, const int numIneqConstraint, const int numEqConstraint, const int numQIneqConstraint, const solverType solverMethod)
{
/** Initialize class variables */
_numVariable = numVariable;
_numEqConstraint = numEqConstraint;
_numIneqConstraint = numIneqConstraint;
_numQIneqConstraint = numQIneqConstraint;
_solverMethod = solverMethod;
_IloInitialized = false;
_convergenceTolQP = 1.0e-8;
_convergenceTolQCP = 1.0e-8;
_optimalityTol = 1.0e-6;
_feasibilityTol = 1.0e-6;
}
CPLEXsolver::CPLEXsolver(const int numVariable, const int numIneqConstraint, const int numEqConstraint, const int numQIneqConstraint)
{
/** Initialize class variables */
_numVariable = numVariable;
_numEqConstraint = numEqConstraint;
_numIneqConstraint = numIneqConstraint;
_numQIneqConstraint = numQIneqConstraint;
_solverMethod = AUTO;
_IloInitialized = false;
_convergenceTolQP = 1.0e-8;
_convergenceTolQCP = 1.0e-8;
_optimalityTol = 1.0e-6;
_feasibilityTol = 1.0e-6;
}
CPLEXsolver::CPLEXsolver()
{
/** Initialize class variables */
_numVariable = -1;
_numEqConstraint = -1;
_numIneqConstraint = -1;
_numQIneqConstraint = -1;
_solverMethod = AUTO;
_IloInitialized = false;
_convergenceTolQP = 1.0e-8;
_convergenceTolQCP = 1.0e-8;
_optimalityTol = 1.0e-6;
_feasibilityTol = 1.0e-6;
}
bool CPLEXsolver::initProblem()
{
/** Check the problem has been initialized */
if ((_numVariable<0) || (_numEqConstraint<0) || (_numIneqConstraint<0) || (_numQIneqConstraint<0))
{
std::cout << "[CPLEXsolver] Number of variables/constraints not initialized" << std::endl;
return false;
}
/** Create and initialize a CPLEX environment */
try
{
// Create environment variables
_IloModel = IloModel(_IloEnv);
_IloVar = IloNumVarArray(_IloEnv);
_IloConstrIneq = IloRangeArray(_IloEnv);
_IloConstrEq = IloRangeArray(_IloEnv);
// Create unknowns without lower/upper bounds
for (int i=0; i<_numVariable; i++)
_IloVar.add(IloNumVar(_IloEnv, -IloInfinity, +IloInfinity));
// Create a dummy objective function
IloExpr objExpr(_IloEnv, 0.0);
_IloObj = IloObjective(_IloEnv, objExpr, IloObjective::Minimize);
_IloModel.add(_IloObj);
objExpr.end();
// Create a dummy problem
_IloCplex = IloCplex(_IloModel);
_IloInitialized = true;
// Set Cplex solver
switch(_solverMethod)
{
case AUTO:
_IloCplex.setParam(IloCplex::Param::RootAlgorithm, IloCplex::Auto);
_IloCplex.setParam(IloCplex::Param::NodeAlgorithm, IloCplex::Auto);
_IloCplex.setParam(IloCplex::Param::Simplex::Tolerances::Optimality, _optimalityTol);
_IloCplex.setParam(IloCplex::Param::Simplex::Tolerances::Feasibility, _feasibilityTol);
_IloCplex.setParam(IloCplex::Param::Network::Tolerances::Optimality, _optimalityTol);
_IloCplex.setParam(IloCplex::Param::Network::Tolerances::Feasibility, _feasibilityTol);
_IloCplex.setParam(IloCplex::Param::Barrier::ConvergeTol, _convergenceTolQP);
_IloCplex.setParam(IloCplex::Param::Barrier::QCPConvergeTol, _convergenceTolQCP);
break;
case PRIMAL:
_IloCplex.setParam(IloCplex::Param::RootAlgorithm, IloCplex::Primal);
_IloCplex.setParam(IloCplex::Param::NodeAlgorithm, IloCplex::Primal);
_IloCplex.setParam(IloCplex::Param::Simplex::Tolerances::Optimality, _optimalityTol);
_IloCplex.setParam(IloCplex::Param::Simplex::Tolerances::Feasibility, _feasibilityTol);
break;
case DUAL:
_IloCplex.setParam(IloCplex::Param::RootAlgorithm, IloCplex::Dual);
_IloCplex.setParam(IloCplex::Param::NodeAlgorithm, IloCplex::Dual);
_IloCplex.setParam(IloCplex::Param::Simplex::Tolerances::Optimality, _optimalityTol);
_IloCplex.setParam(IloCplex::Param::Simplex::Tolerances::Feasibility, _feasibilityTol);
break;
case NETWORK:
_IloCplex.setParam(IloCplex::Param::RootAlgorithm, IloCplex::Network);
_IloCplex.setParam(IloCplex::Param::NodeAlgorithm, IloCplex::Network);
_IloCplex.setParam(IloCplex::Param::Network::Tolerances::Optimality, _optimalityTol);
_IloCplex.setParam(IloCplex::Param::Network::Tolerances::Feasibility, _feasibilityTol);
break;
case BARRIER:
_IloCplex.setParam(IloCplex::Param::RootAlgorithm, IloCplex::Barrier);
_IloCplex.setParam(IloCplex::Param::NodeAlgorithm, IloCplex::Barrier);
_IloCplex.setParam(IloCplex::Param::Barrier::ConvergeTol, _convergenceTolQP);
_IloCplex.setParam(IloCplex::Param::Barrier::QCPConvergeTol, _convergenceTolQCP);
break;
case SIFTING:
_IloCplex.setParam(IloCplex::Param::RootAlgorithm, IloCplex::Sifting);
_IloCplex.setParam(IloCplex::Param::NodeAlgorithm, IloCplex::Sifting);
break;
case CONCURRENT:
_IloCplex.setParam(IloCplex::Param::RootAlgorithm, IloCplex::Concurrent);
_IloCplex.setParam(IloCplex::Param::NodeAlgorithm, IloCplex::Concurrent);
break;
default:
_IloCplex.setParam(IloCplex::Param::RootAlgorithm, IloCplex::Auto);
_IloCplex.setParam(IloCplex::Param::NodeAlgorithm, IloCplex::Auto);
_IloCplex.setParam(IloCplex::Param::Simplex::Tolerances::Optimality, _optimalityTol);
_IloCplex.setParam(IloCplex::Param::Simplex::Tolerances::Feasibility, _feasibilityTol);
_IloCplex.setParam(IloCplex::Param::Network::Tolerances::Optimality, _optimalityTol);
_IloCplex.setParam(IloCplex::Param::Network::Tolerances::Feasibility, _feasibilityTol);
_IloCplex.setParam(IloCplex::Param::Barrier::ConvergeTol, _convergenceTolQP);
_IloCplex.setParam(IloCplex::Param::Barrier::QCPConvergeTol, _convergenceTolQCP);
}
}
catch (IloException& e)
{
std::cout << "[CPLEXsolver] Error initializing Ilog CPLEX:" << std::endl;
std::cout << "\t [" << e << "]" << std::endl;
return false;
}
/** Display CPLEX solver parameters */
std::cout << std::endl;
std::cout << "---------- CPLEX solver parameters ----------" << std::endl;
std::cout << "Initial algorithm: " << _IloCplex.getParam(IloCplex::Param::RootAlgorithm) << std::endl;
std::cout << "Subproblem algorithm: " << _IloCplex.getParam(IloCplex::Param::NodeAlgorithm) << std::endl;
std::cout << "Optimality tolerance: " << _IloCplex.getParam(IloCplex::Param::Simplex::Tolerances::Optimality) << std::endl;
std::cout << "Feasibility tolerance: " << _IloCplex.getParam(IloCplex::Param::Simplex::Tolerances::Feasibility) << std::endl;
std::cout << "Optimality tolerance (network): " << _IloCplex.getParam(IloCplex::Param::Network::Tolerances::Optimality) << std::endl;
std::cout << "Feasibility tolerance (network): " << _IloCplex.getParam(IloCplex::Param::Network::Tolerances::Feasibility) << std::endl;
std::cout << "QP convergence tolerance (barrier): " << _IloCplex.getParam(IloCplex::Param::Barrier::ConvergeTol) << std::endl;
std::cout << "QCP convergence tolerance (barrier): " << _IloCplex.getParam(IloCplex::Param::Barrier::QCPConvergeTol) << std::endl;
std::cout << "---------------------------------------------" << std::endl << std::endl;
return true;
}
bool CPLEXsolver::setProblem(const Ref<const MatrixXd> hessian, const Ref<const VectorXd> gradient)
// Set an optimization problem characterized by cost function only
// Cost function min 0.5*xT*H*x+fT*x
{
/** Check that the problem has been initialized */
if (!_IloInitialized)
{
std::cout << "[CPLEXsolver] Call initProblem before setProblem" << std::endl;
return false;
}
if ((hessian.rows()<1) || (hessian.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty hessian" << std::endl;
return false;
}
if ((gradient.rows()<1) || (gradient.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty gradient" << std::endl;
return false;
}
if (hessian.rows()!=hessian.cols())
{
std::cout << "[CPLEXsolver] Cannot setProblem with a non square hessian" << std::endl;
return false;
}
if ((gradient.rows()!=hessian.rows()) || (gradient.cols()>1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a gradient having wrong dimensions" << std::endl;
return false;
}
/** Set the QP problem */
try
{
// Check and update the number of unknowns
if (_numVariable>hessian.cols())
_IloVar.remove(_IloVar.getSize()-(_numVariable-hessian.cols()), _numVariable-hessian.cols());
else if (_numVariable<hessian.cols())
for (int i=0; i<hessian.cols()-_numVariable; i++)
_IloVar.add(IloNumVar(_IloEnv, -IloInfinity, +IloInfinity));
_numVariable = _IloVar.getSize();
// Remove unknowns lower/upper bounds
for (int i=0; i<_numVariable; i++)
_IloVar[i].setBounds(-IloInfinity, +IloInfinity);
// Check and remove inequality constraints
if (_numIneqConstraint>0)
{
_IloModel.remove(_IloConstrIneq);
_IloConstrIneq.endElements();
_numIneqConstraint = 0;
}
// Check and remove equality constraints
if (_numEqConstraint>0)
{
_IloModel.remove(_IloConstrEq);
_IloConstrEq.endElements();
_numEqConstraint = 0;
}
// Modify the objective function
IloExpr objExpr(_IloEnv, 0.0);
for (int i=0; i<_numVariable; i++) // Quadratic part of the objective function
for (int j=i; j<_numVariable; j++)
{
if (i==j)
objExpr += 0.5*hessian(i,j)*_IloVar[i]*_IloVar[j];
else
objExpr += hessian(i,j)*_IloVar[i]*_IloVar[j];
}
for (int i=0; i<_numVariable; i++) // Linear part of the objective function
objExpr += gradient(i)*_IloVar[i];
_IloObj.setExpr(objExpr);
objExpr.end();
}
catch (IloException& e)
{
std::cout << "[CPLEXsolver] CPlex failed setting up the QP problem:" << std::endl;
std::cout << "\t [" << e << "]" << std::endl;
return false;
}
return true;
}
bool CPLEXsolver::setProblem(const Ref<const MatrixXd> hessian, const Ref<const VectorXd> gradient, const Ref<const MatrixXd> A, const Ref<const VectorXd> B)
// Set an optimization problem characterized by cost function and linear inequality constraints
// Cost function min 0.5*xT*H*x+fT*x
// Inequality constraints Ax <= B
{
/** Check that the problem has been initialized */
if (!_IloInitialized)
{
std::cout << "[CPLEXsolver] Call initProblem before setProblem" << std::endl;
return false;
}
if ((hessian.rows()<1) || (hessian.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty hessian" << std::endl;
return false;
}
if ((gradient.rows()<1) || (gradient.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty gradient" << std::endl;
return false;
}
if (hessian.rows()!=hessian.cols())
{
std::cout << "[CPLEXsolver] Cannot setProblem with a non square hessian" << std::endl;
return false;
}
if ((gradient.rows()!=hessian.rows()) || (gradient.cols()>1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a gradient having wrong dimensions" << std::endl;
return false;
}
if ((A.rows()<1) || (A.cols()<1) || (B.rows()<1) || (B.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty set of inequality constraints" << std::endl;
return false;
}
if ((A.cols()!=hessian.rows()) || (A.rows()!=B.rows()) || (B.cols()!=1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a set of inequality constraints having wrong dimensions" << std::endl;
return false;
}
/** Set the QP problem */
try
{
// Check and update the number of optimization variables
if (_numVariable>hessian.cols())
_IloVar.remove(_IloVar.getSize()-(_numVariable-hessian.cols()), _numVariable-hessian.cols());
else if (_numVariable<hessian.cols())
for (int i=0; i<hessian.cols()-_numVariable; i++)
_IloVar.add(IloNumVar(_IloEnv, -IloInfinity, +IloInfinity));
_numVariable = _IloVar.getSize();
// Remove unknowns lower/upper bounds
for (int i=0; i<_numVariable; i++)
_IloVar[i].setBounds(-IloInfinity, +IloInfinity);
// Check and remove equality constraints
if (_numEqConstraint>0)
{
_IloModel.remove(_IloConstrEq);
_IloConstrEq.endElements();
_numEqConstraint = 0;
}
// Modify the objective function
IloExpr objExpr(_IloEnv, 0.0);
for (int i=0; i<_numVariable; i++) // Quadratic part of the objective function
for (int j=i; j<_numVariable; j++)
{
if (i==j)
objExpr += 0.5*hessian(i,j)*_IloVar[i]*_IloVar[j];
else
objExpr += hessian(i,j)*_IloVar[i]*_IloVar[j];
}
for (int i=0; i<_numVariable; i++) // Linear part of the objective function
objExpr += gradient(i)*_IloVar[i];
_IloObj.setExpr(objExpr);
objExpr.end();
// Add the inequality constraints
if (_numIneqConstraint!=A.rows())
{
// Remove previous constraints
_IloModel.remove(_IloConstrIneq);
_IloConstrIneq.endElements();
_numIneqConstraint = A.rows();
}
for (int i=0; i<_numIneqConstraint; i++)
{
IloExpr conExpr(_IloEnv, 0.0);
for (int j=0; j<_numVariable; j++)
conExpr += A(i,j)*_IloVar[j];
_IloConstrIneq.add(IloRange(_IloEnv, conExpr, B(i)));
conExpr.end();
}
_IloModel.add(_IloConstrIneq);
}
catch (IloException& e)
{
std::cout << "[CPLEXsolver] CPlex failed setting up the QP problem:" << std::endl;
std::cout << "\t [" << e << "]" << std::endl;
return false;
}
return true;
}
bool CPLEXsolver::setProblem(const Ref<const MatrixXd> hessian, const Ref<const VectorXd> gradient, const Ref<const MatrixXd> A, const Ref<const VectorXd> B,
const std::vector<VectorXd>& l, const std::vector<MatrixXd> Q, const std::vector<double>& r)
// Set an optimization problem characterized by cost function, linear inequality constraints and quadratic inequality constraints
// Cost function min 0.5*xT*H*x+fT*x
// Linear inequality constraints Ax <= B
// Quadratic inequality constraints aTi*x+xT*Qi*x <= ri i=1,...,q
{
/** Check that the problem has been initialized */
if (!_IloInitialized)
{
std::cout << "[CPLEXsolver] Call initProblem before setProblem" << std::endl;
return false;
}
if ((hessian.rows()<1) || (hessian.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty hessian" << std::endl;
return false;
}
if ((gradient.rows()<1) || (gradient.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty gradient" << std::endl;
return false;
}
if (hessian.rows()!=hessian.cols())
{
std::cout << "[CPLEXsolver] Cannot setProblem with a non square hessian" << std::endl;
return false;
}
if ((gradient.rows()!=hessian.rows()) || (gradient.cols()>1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a gradient having wrong dimensions" << std::endl;
return false;
}
if ((A.rows()<1) || (A.cols()<1) || (B.rows()<1) || (B.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty set of linear inequality constraints" << std::endl;
return false;
}
if ((A.cols()!=hessian.rows()) || (A.rows()!=B.rows()) || (B.cols()!=1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a set of linear inequality constraints having wrong dimensions" << std::endl;
return false;
}
if ((l.size()!=_numQIneqConstraint) || (Q.size()!=_numQIneqConstraint) || (r.size()!=_numQIneqConstraint))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a wrong number of quadratic inequality constraints" << std::endl;
return false;
}
for (int k=0; k<l.size(); k++)
{
if ((l.at(k).rows()<1) || (l.at(k).cols()<1) || (Q.at(k).rows()<1) || (Q.at(k).cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty set of quadratic inequality constraints" << std::endl;
return false;
}
if ((l.at(k).rows()!=hessian.rows()) || (l.at(k).cols()!=1) || (Q.at(k).rows()!=hessian.rows()) || (Q.at(k).cols()!=hessian.rows()))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a set of quadratic inequality constraints having wrong dimensions" << std::endl;
return false;
}
}
/** Set the QP problem */
try
{
// Check and update the number of optimization variables
if (_numVariable>hessian.cols())
_IloVar.remove(_IloVar.getSize()-(_numVariable-hessian.cols()), _numVariable-hessian.cols());
else if (_numVariable<hessian.cols())
for (int i=0; i<hessian.cols()-_numVariable; i++)
_IloVar.add(IloNumVar(_IloEnv, -IloInfinity, +IloInfinity));
_numVariable = _IloVar.getSize();
// Remove unknowns lower/upper bounds
for (int i=0; i<_numVariable; i++)
_IloVar[i].setBounds(-IloInfinity, +IloInfinity);
// Check and remove equality constraints
if (_numEqConstraint>0)
{
_IloModel.remove(_IloConstrEq);
_IloConstrEq.endElements();
_numEqConstraint = 0;
}
// Modify the objective function
IloExpr objExpr(_IloEnv, 0.0);
for (int i=0; i<_numVariable; i++) // Quadratic part of the objective function
for (int j=i; j<_numVariable; j++)
{
if (i==j)
objExpr += 0.5*hessian(i,j)*_IloVar[i]*_IloVar[j];
else
objExpr += hessian(i,j)*_IloVar[i]*_IloVar[j];
}
for (int i=0; i<_numVariable; i++) // Linear part of the objective function
objExpr += gradient(i)*_IloVar[i];
_IloObj.setExpr(objExpr);
objExpr.end();
// Add the inequality constraints
if ((_numIneqConstraint+_numQIneqConstraint)!=(A.rows()+r.size()))
{
// Remove previous constraints
_IloModel.remove(_IloConstrIneq);
_IloConstrIneq.endElements();
_numIneqConstraint = A.rows();
_numQIneqConstraint = r.size();
}
for (int i=0; i<_numIneqConstraint; i++)
{
IloExpr conExpr(_IloEnv, 0.0);
for (int j=0; j<_numVariable; j++)
conExpr += A(i,j)*_IloVar[j];
_IloConstrIneq.add(IloRange(_IloEnv, conExpr, B(i)));
conExpr.end();
}
for (int i=0; i<_numQIneqConstraint; i++)
{
IloExpr conExpr(_IloEnv, 0.0);
// Linear part of the constraint aiT*x
for (int j=0; j<_numVariable; j++)
conExpr += l.at(i)(j)*_IloVar[j];
// Quadratic part of the constraint xT*Q*x
for (int j=0; j<_numVariable; j++)
for (int k=0; k<_numVariable; k++)
conExpr += Q.at(i)(j,k)*_IloVar[j]*_IloVar[k];
_IloConstrIneq.add(IloRange(_IloEnv, conExpr, r.at(i)));
conExpr.end();
}
_IloModel.add(_IloConstrIneq);
}
catch (IloException& e)
{
std::cout << "[CPLEXsolver] CPlex failed setting up the QP problem:" << std::endl;
std::cout << "\t [" << e << "]" << std::endl;
return false;
}
return true;
}
bool CPLEXsolver::setProblem(const std::vector<double>& lowerBound, const std::vector<double>& upperBound, const Ref<const MatrixXd> hessian,
const Ref<const VectorXd> gradient)
// Set an optimization problem characterized by cost function and lower/upper bound
// Cost function min 0.5*xT*H*x+fT*x
// Variable constraints lb <= x <= ub
{
/** Check that the problem has been initialized */
if (!_IloInitialized)
{
std::cout << "[CPLEXsolver] Call initProblem before setProblem" << std::endl;
return false;
}
if ((hessian.rows()<1) || (hessian.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty hessian" << std::endl;
return false;
}
if ((gradient.rows()<1) || (gradient.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty gradient" << std::endl;
return false;
}
if (hessian.rows()!=hessian.cols())
{
std::cout << "[CPLEXsolver] Cannot setProblem with a non square hessian" << std::endl;
return false;
}
if ((gradient.rows()!=hessian.rows()) || (gradient.cols()>1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a gradient having wrong dimensions" << std::endl;
return false;
}
if ((lowerBound.size()!=hessian.rows()) || (upperBound.size()!=hessian.rows()))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a wrong dimension on lower/upper bounds" << std::endl;
return false;
}
/** Set the QP problem */
try
{
// Check and update the number of optimization variables
if (_numVariable>hessian.cols())
_IloVar.remove(_IloVar.getSize()-(_numVariable-hessian.cols()), _numVariable-hessian.cols());
else if (_numVariable<hessian.cols())
for (int i=0; i<hessian.cols()-_numVariable; i++)
_IloVar.add(IloNumVar(_IloEnv, -IloInfinity, +IloInfinity));
_numVariable = _IloVar.getSize();
// Modify unknowns lower/upper bounds
for (int i=0; i<_numVariable; i++)
_IloVar[i].setBounds(lowerBound.at(i), upperBound.at(i));
// Check and remove inequality constraints
if (_numIneqConstraint>0)
{
_IloModel.remove(_IloConstrIneq);
_IloConstrIneq.endElements();
_numIneqConstraint = 0;
}
// Check and remove equality constraints
if (_numEqConstraint>0)
{
_IloModel.remove(_IloConstrEq);
_IloConstrEq.endElements();
_numEqConstraint = 0;
}
// Modify the objective function
IloExpr objExpr(_IloEnv, 0.0);
for (int i=0; i<_numVariable; i++) // Quadratic part of the objective function
for (int j=i; j<_numVariable; j++)
{
if (i==j)
objExpr += 0.5*hessian(i,j)*_IloVar[i]*_IloVar[j];
else
objExpr += hessian(i,j)*_IloVar[i]*_IloVar[j];
}
for (int i=0; i<_numVariable; i++) // Linear part of the objective function
objExpr += gradient(i)*_IloVar[i];
_IloObj.setExpr(objExpr);
objExpr.end();
}
catch (IloException& e)
{
std::cout << "[CPLEXsolver] CPlex failed setting up the QP problem:" << std::endl;
std::cout << "\t [" << e << "]" << std::endl;
return false;
}
return true;
}
bool CPLEXsolver::setProblem(const std::vector<double>& lowerBound, const std::vector<double>& upperBound, const Ref<const MatrixXd> hessian,
const Ref<const VectorXd> gradient, const std::vector<VectorXd>& l, const std::vector<MatrixXd> Q,
const std::vector<double>& r)
// Set an optimization problem characterized by cost function, lower/upper bound and quadratic constraints
// Cost function min 0.5*xT*H*x+fT*x
// Variable constraints lb <= x <= ub
// Quadratic inequality constraints aTi*x+xT*Qi*x <= ri i=1,...,q
{
/** Check that the problem has been initialized */
if (!_IloInitialized)
{
std::cout << "[CPLEXsolver] Call initProblem before setProblem" << std::endl;
return false;
}
if ((hessian.rows()<1) || (hessian.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty hessian" << std::endl;
return false;
}
if ((gradient.rows()<1) || (gradient.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty gradient" << std::endl;
return false;
}
if (hessian.rows()!=hessian.cols())
{
std::cout << "[CPLEXsolver] Cannot setProblem with a non square hessian" << std::endl;
return false;
}
if ((gradient.rows()!=hessian.rows()) || (gradient.cols()>1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a gradient having wrong dimensions" << std::endl;
return false;
}
if ((lowerBound.size()!=hessian.rows()) || (upperBound.size()!=hessian.rows()))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a wrong dimension on lower/upper bounds" << std::endl;
return false;
}
if ((l.size()!=_numQIneqConstraint) || (Q.size()!=_numQIneqConstraint) || (r.size()!=_numQIneqConstraint))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a wrong number of quadratic inequality constraints" << std::endl;
return false;
}
for (int k=0; k<l.size(); k++)
{
if ((l.at(k).rows()<1) || (l.at(k).cols()<1) || (Q.at(k).rows()<1) || (Q.at(k).cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty set of quadratic inequality constraints" << std::endl;
return false;
}
if ((l.at(k).rows()!=hessian.rows()) || (l.at(k).cols()!=1) || (Q.at(k).rows()!=hessian.rows()) || (Q.at(k).cols()!=hessian.rows()))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a set of quadratic inequality constraints having wrong dimensions" << std::endl;
return false;
}
}
/** Set the QP problem */
try
{
// Check and update the number of optimization variables
if (_numVariable>hessian.cols())
_IloVar.remove(_IloVar.getSize()-(_numVariable-hessian.cols()), _numVariable-hessian.cols());
else if (_numVariable<hessian.cols())
for (int i=0; i<hessian.cols()-_numVariable; i++)
_IloVar.add(IloNumVar(_IloEnv, -IloInfinity, +IloInfinity));
_numVariable = _IloVar.getSize();
// Modify unknowns lower/upper bounds
for (int i=0; i<_numVariable; i++)
_IloVar[i].setBounds(lowerBound.at(i), upperBound.at(i));
// Check and remove equality constraints
if (_numEqConstraint>0)
{
_IloModel.remove(_IloConstrEq);
_IloConstrEq.endElements();
_numEqConstraint = 0;
}
// Modify the objective function
IloExpr objExpr(_IloEnv, 0.0);
for (int i=0; i<_numVariable; i++) // Quadratic part of the objective function
for (int j=i; j<_numVariable; j++)
{
if (i==j)
objExpr += 0.5*hessian(i,j)*_IloVar[i]*_IloVar[j];
else
objExpr += hessian(i,j)*_IloVar[i]*_IloVar[j];
}
for (int i=0; i<_numVariable; i++) // Linear part of the objective function
objExpr += gradient(i)*_IloVar[i];
_IloObj.setExpr(objExpr);
objExpr.end();
// Update and modify the inequality constraints
if (_numQIneqConstraint!=r.size())
{
// Remove previous constraints
_IloModel.remove(_IloConstrIneq);
_IloConstrIneq.endElements();
_numQIneqConstraint = r.size();
}
for (int i=0; i<_numQIneqConstraint; i++)
{
IloExpr conExpr(_IloEnv, 0.0);
// Linear part of the constraint aiT*x
for (int j=0; j<_numVariable; j++)
conExpr += l.at(i)(j)*_IloVar[j];
// Quadratic part of the constraint xT*Q*x
for (int j=0; j<_numVariable; j++)
for (int k=0; k<_numVariable; k++)
conExpr += Q.at(i)(j,k)*_IloVar[j]*_IloVar[k];
_IloConstrIneq.add(IloRange(_IloEnv, conExpr, r.at(i)));
conExpr.end();
}
_IloModel.add(_IloConstrIneq);
}
catch (IloException& e)
{
std::cout << "[CPLEXsolver] CPlex failed setting up the QP problem:" << std::endl;
std::cout << "\t [" << e << "]" << std::endl;
return false;
}
return true;
}
bool CPLEXsolver::setProblem(const std::vector<double>& lowerBound, const std::vector<double>& upperBound, const Ref<const MatrixXd> hessian,
const Ref<const VectorXd> gradient, const Ref<const MatrixXd> A, const Ref<const VectorXd> B)
// Set an optimization problem characterized by cost function and inequality constraints
// Cost function min 0.5*xT*H*x+fT*x
// Variable constraints lb <= x <= ub
// Inequality constraints Ax <= B
{
/** Check that the problem has been initialized */
if (!_IloInitialized)
{
std::cout << "[CPLEXsolver] Call initProblem before setProblem" << std::endl;
return false;
}
if ((hessian.rows()<1) || (hessian.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty hessian" << std::endl;
return false;
}
if ((gradient.rows()<1) || (gradient.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty gradient" << std::endl;
return false;
}
if (hessian.rows()!=hessian.cols())
{
std::cout << "[CPLEXsolver] Cannot setProblem with a non square hessian" << std::endl;
return false;
}
if ((gradient.rows()!=hessian.rows()) || (gradient.cols()>1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a gradient having wrong dimensions" << std::endl;
return false;
}
if ((lowerBound.size()!=hessian.rows()) || (upperBound.size()!=hessian.rows()))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a wrong dimension on lower/upper bounds" << std::endl;
return false;
}
if ((A.rows()<1) || (A.cols()<1) || (B.rows()<1) || (B.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty set of inequality constraints" << std::endl;
return false;
}
if ((A.cols()!=hessian.rows()) || (A.rows()!=B.rows()) || (B.cols()!=1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a set of inequality constraints having wrong dimensions" << std::endl;
return false;
}
/** Set the QP problem */
try
{
// Check and update the number of optimization variables
if (_numVariable>hessian.cols())
_IloVar.remove(_IloVar.getSize()-(_numVariable-hessian.cols()), _numVariable-hessian.cols());
else if (_numVariable<hessian.cols())
for (int i=0; i<hessian.cols()-_numVariable; i++)
_IloVar.add(IloNumVar(_IloEnv, -IloInfinity, +IloInfinity));
_numVariable = _IloVar.getSize();
// Modify unknowns lower/upper bounds
for (int i=0; i<_numVariable; i++)
_IloVar[i].setBounds(lowerBound.at(i), upperBound.at(i));
// Check and remove equality constraints
if (_numEqConstraint>0)
{
_IloModel.remove(_IloConstrEq);
_IloConstrEq.endElements();
_numEqConstraint = 0;
}
// Modify the objective function
IloExpr objExpr(_IloEnv, 0.0);
for (int i=0; i<_numVariable; i++) // Quadratic part of the objective function
for (int j=i; j<_numVariable; j++)
{
if (i==j)
objExpr += 0.5*hessian(i,j)*_IloVar[i]*_IloVar[j];
else
objExpr += hessian(i,j)*_IloVar[i]*_IloVar[j];
}
for (int i=0; i<_numVariable; i++) // Linear part of the objective function
objExpr += gradient(i)*_IloVar[i];
_IloObj.setExpr(objExpr);
objExpr.end();
// Add the inequality constraints
if (_numIneqConstraint!=A.rows())
{
// Remove previous constraints
_IloModel.remove(_IloConstrIneq);
_IloConstrIneq.endElements();
_numIneqConstraint = A.rows();
}
for (int i=0; i<_numIneqConstraint; i++)
{
IloExpr conExpr(_IloEnv, 0.0);
for (int j=0; j<_numVariable; j++)
conExpr += A(i,j)*_IloVar[j];
_IloConstrIneq.add(IloRange(_IloEnv, conExpr, B(i)));
conExpr.end();
}
_IloModel.add(_IloConstrIneq);
}
catch (IloException& e)
{
std::cout << "[CPLEXsolver] CPlex failed setting up the QP problem:" << std::endl;
std::cout << "\t [" << e << "]" << std::endl;
return false;
}
return true;
}
bool CPLEXsolver::setProblem(const std::vector<double>& lowerBound, const std::vector<double>& upperBound, const Ref<const MatrixXd> hessian,
const Ref<const VectorXd> gradient, const Ref<const MatrixXd> A, const Ref<const VectorXd> B,
const std::vector<VectorXd>& l, const std::vector<MatrixXd> Q, const std::vector<double>& r)
// Set an optimization problem characterized by cost function and inequality constraints
// Cost function min 0.5*xT*H*x+fT*x
// Variable constraints lb <= x <= ub
// Linear inequality constraints Ax <= B
// Quadratic inequality constraints aTi*x+xT*Qi*x <= ri i=1,...,q
{
/** Check that the problem has been initialized */
if (!_IloInitialized)
{
std::cout << "[CPLEXsolver] Call initProblem before setProblem" << std::endl;
return false;
}
if ((hessian.rows()<1) || (hessian.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty hessian" << std::endl;
return false;
}
if ((gradient.rows()<1) || (gradient.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty gradient" << std::endl;
return false;
}
if (hessian.rows()!=hessian.cols())
{
std::cout << "[CPLEXsolver] Cannot setProblem with a non square hessian" << std::endl;
return false;
}
if ((gradient.rows()!=hessian.rows()) || (gradient.cols()>1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a gradient having wrong dimensions" << std::endl;
return false;
}
if ((lowerBound.size()!=hessian.rows()) || (upperBound.size()!=hessian.rows()))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a wrong dimension on lower/upper bounds" << std::endl;
return false;
}
if ((A.rows()<1) || (A.cols()<1) || (B.rows()<1) || (B.cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty set of inequality constraints" << std::endl;
return false;
}
if ((A.cols()!=hessian.rows()) || (A.rows()!=B.rows()) || (B.cols()!=1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a set of inequality constraints having wrong dimensions" << std::endl;
return false;
}
if ((l.size()!=_numQIneqConstraint) || (Q.size()!=_numQIneqConstraint) || (r.size()!=_numQIneqConstraint))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a wrong number of quadratic inequality constraints" << std::endl;
return false;
}
for (int k=0; k<l.size(); k++)
{
if ((l.at(k).rows()<1) || (l.at(k).cols()<1) || (Q.at(k).rows()<1) || (Q.at(k).cols()<1))
{
std::cout << "[CPLEXsolver] Cannot setProblem with an empty set of quadratic inequality constraints" << std::endl;
return false;
}
if ((l.at(k).rows()!=hessian.rows()) || (l.at(k).cols()!=1) || (Q.at(k).rows()!=hessian.rows()) || (Q.at(k).cols()!=hessian.rows()))
{
std::cout << "[CPLEXsolver] Cannot setProblem with a set of quadratic inequality constraints having wrong dimensions" << std::endl;
return false;
}
}
/** Set the QP problem */
try
{
// Check and update the number of optimization variables
if (_numVariable>hessian.cols())
_IloVar.remove(_IloVar.getSize()-(_numVariable-hessian.cols()), _numVariable-hessian.cols());
else if (_numVariable<hessian.cols())
for (int i=0; i<hessian.cols()-_numVariable; i++)
_IloVar.add(IloNumVar(_IloEnv, -IloInfinity, +IloInfinity));
_numVariable = _IloVar.getSize();