CxUnivarHandler::CxUnivarHandler(EnvPtr , ProblemPtr problem)
: eTol_(1e-6),
vTol_(1e-5)
{
problem_ = problem;
logger_ = (LoggerPtr) new Logger((LogLevel) 0 );
tmpX_.resize(problem->getNumVars(), 0.0);
grad_.resize(problem->getNumVars(), 0.0);
}
void loadProblem(EnvPtr env, MINOTAUR_AMPL::AMPLInterface* iface,
ProblemPtr &oinst, double *obj_sense)
{
Timer *timer = env->getNewTimer();
OptionDBPtr options = env->getOptions();
JacobianPtr jac;
HessianOfLagPtr hess;
const std::string me("qg: ");
timer->start();
oinst = iface->readInstance(options->findString("problem_file")->getValue());
env->getLogger()->msgStream(LogInfo) << me
<< "time used in reading instance = " << std::fixed
<< std::setprecision(2) << timer->query() << std::endl;
// display the problem
oinst->calculateSize();
if (options->findBool("display_problem")->getValue()==true) {
oinst->write(env->getLogger()->msgStream(LogNone), 12);
}
if (options->findBool("display_size")->getValue()==true) {
oinst->writeSize(env->getLogger()->msgStream(LogNone));
}
// create the jacobian
if (false==options->findBool("use_native_cgraph")->getValue()) {
jac = (MINOTAUR_AMPL::AMPLJacobianPtr)
new MINOTAUR_AMPL::AMPLJacobian(iface);
oinst->setJacobian(jac);
// create the hessian
hess = (MINOTAUR_AMPL::AMPLHessianPtr)
new MINOTAUR_AMPL::AMPLHessian(iface);
oinst->setHessian(hess);
}
// set initial point
oinst->setInitialPoint(iface->getInitialPoint(),
oinst->getNumVars()-iface->getNumDefs());
if (oinst->getObjective() &&
oinst->getObjective()->getObjectiveType()==Maximize) {
*obj_sense = -1.0;
env->getLogger()->msgStream(LogInfo) << me
<< "objective sense: maximize (will be converted to Minimize)"
<< std::endl;
} else {
*obj_sense = 1.0;
env->getLogger()->msgStream(LogInfo) << me
<< "objective sense: minimize" << std::endl;
}
delete timer;
}
CxUnivarHandler::CxUnivarHandler(EnvPtr , ProblemPtr problem)
: eTol_(1e-6),
vTol_(1e-5)
{
problem_ = problem;
//logger_ = (LoggerPtr) new Logger((LogLevel)
// env->getOptions()->findInt("handler_log_level")->getValue());
logger_ = (LoggerPtr) new Logger((LogLevel) 0 );
tmpX_.resize(problem->getNumVars(), 0.0);
grad_.resize(problem->getNumVars(), 0.0);
}
int main()
{
// Generate output.
ofstream output;
output.open("numknapcov.txt");
// Generate input.
ifstream input;
input.open("list.txt");
// Check if input is opened succesfully.
if (input.is_open() == false) {
cerr << "Input file read error." << endl;
output << "Input file read error." << endl;
exit(0);
}
/********************************************************************************/
// Headers for output data.
output << "Statistics of knapsack cover cuts applied to root relaxation." << endl;
output << "problem " << "vars " << "cons " << "lincons " << "knapcons " << "knapcov "
<< "knaps " << "totalcuts " << "cuts " << "violknapcuts " << "initobj "
<< "endobj " << "gapclosed " << "timeinit " << "timecut " << "timemod"
<< endl;
/********************************************************************************/
// loop to test all problems in list.txt
while (input.good()) {
// problem name
string pname;
getline(input, pname);
// At the end of file just exit from loop.
if (pname.empty()) {
break;
}
cout << "Problem considered is: " << pname << endl;
// Real stuff begins.
// Ampl interface, jacobian and hessian.
MINOTAUR_AMPL::AMPLInterfacePtr iface = MINOTAUR_AMPL::AMPLInterfacePtr();
JacobianPtr jPtr; //! Jacobian read from AMPL
HessianOfLagPtr hPtr; //! Hessian read from AMPL
// environment, timers and options:
EnvPtr env = (EnvPtr) new Environment();
OptionDBPtr options;
// problem to be solved.
ProblemPtr minlp;
// solver pointers, including status.
FilterSQPEngine e(env);
EngineStatus status;
// Presolver.
PresolverPtr pres;
// give parameters.
UInt argc2 = 2;
std::string arg1 = "bnb";
std::string arg2 = pname;
char** argv2 = new char* [2];
argv2[0] = &arg1[0];
argv2[1] = &arg2[0];
// Default options
env->getOptions()->findBool("presolve")->setValue(false);
env->getOptions()->findBool("use_native_cgraph")->setValue(true);
env->getOptions()->findBool("nl_presolve")->setValue(false);
// parse options
env->readOptions(argc2, argv2);
options = env->getOptions();
options->findString("interface_type")->setValue("AMPL");
// read minlp from AMPL.
iface = (MINOTAUR_AMPL::AMPLInterfacePtr) new MINOTAUR_AMPL::AMPLInterface(env);
minlp = iface->readInstance(pname);
// Timer is obtained.
Timer * timer = env->getNewTimer();
// Nonlinearize objective function.
Bool MIPCONSIDERED = false;
if (MIPCONSIDERED == true) {
ObjectivePtr initobjfun = minlp->getObjective();
if (initobjfun->getObjectiveType() == Maximize) {
cerr << "Objective type is Maximize, change it to Minimize." << endl;
exit(0);
}
LinearFunctionPtr lfinitobj = initobjfun->getLinearFunction();
// NonlinearFunctionPtr nlfobj = (NonlinearFunctionPtr) new NonlinearFunction();
CGraphPtr nlfobj = (CGraphPtr) new CGraph();
logobj(lfinitobj, nlfobj);
FunctionPtr logobjfun = (FunctionPtr) new Function(nlfobj);
ObjectiveType otyp = Minimize;
minlp->changeObj(logobjfun, 0);
}
minlp->calculateSize();
minlp->prepareForSolve();
// Change format of problem to be suitable for Minotaur.
HandlerVector handlers;
// Use presolver to standardize problem.
//pres = (PresolverPtr) new Presolver(minlp, env, handlers);
//pres->standardize();
minlp->calculateSize();
minlp->prepareForSolve();
minlp->setJacobian(jPtr);
minlp->setHessian(hPtr);
minlp->setNativeDer();
minlp->calculateSize();
minlp->prepareForSolve();
minlp->setNativeDer();
//minlp->write(std::cout);
/**************************************************************/
// Given problem statistics .
// Number of variables.
UInt numvars = minlp->getNumVars();
// number of constraints.
UInt numcons = minlp->getNumCons();
// linear constraints.
UInt numlin = minlp->getNumLinCons();
/*************************************************************/
// set option for engine to resolve to solve NLP repeatedly.
// Probbaly does nothing.
e.setOptionsForRepeatedSolve();
// load problem.
e.load(minlp);
// Solve problem.
timer->start();
status = e.solve();
/********************************************************************/
// Solution time of relaxation.
Double timeinit = timer->query();
timer->stop();
// Solution objective value
Double initobj = e.getSolutionValue();
/********************************************************************/
std::cout << "Relaxation objective value = " << initobj << std::endl;
// Get solution from engine.
ConstSolutionPtr sol = e.getSolution();
// Construct relaxation.
RelaxationPtr rel = (RelaxationPtr) new Relaxation(minlp);
// Time for cut generation.
timer->start();
// Generate kanpsack cover cuts.
CoverCutGeneratorPtr knapgen =
(CoverCutGeneratorPtr) new CoverCutGenerator(rel, sol, env);
/*******************************************************************/
Double timecut = timer->query();
timer->stop();
/*******************************************************************/
// Get statistics of cut generator.
ConstCovCutGenStatsPtr knapstats = knapgen->getStats();
/*******************************************************************/
// Knapsack cut generator statistics.
// knapsack constraints.
UInt numknap = (knapgen->getKnapsackList())->getNumKnaps();
// knapsacks that has cover sets.
UInt numknapcov = knapgen->getNumCons();
// knapsack subproblems solved, i.e number of lifting subproblems solved.
UInt knaps = knapstats->knaps;
// cover cuts including duplicates.
UInt totalcuts = knapstats->totalcuts;
// cuts without duplicates.
UInt numknapcuts = knapstats->cuts;
// violated cuts.
UInt violknapcuts = knapstats->violated;
/*******************************************************************/
std::cout << "Number of knapsack cover cuts to be applied is: "
<< knapstats->violated << std::endl;
// Get the violated cuts from generator.
CutVector knapcuts = knapgen->getViolatedCutList();
// Iterators for cuts
CutVectorConstIter it;
CutVectorConstIter begin = knapcuts.begin();
CutVectorConstIter end = knapcuts.end();
// Apply the cuts to the problem.
// Violation list.
DoubleVector knapviols = knapgen->getViolList();
UInt curknap = 0;
Double maxviol = 0.0;
for (it=begin; it!=end; ++it) {
std::cout << "Violation obtained from this constraint is: "
<< knapviols[curknap] << std::endl;
ConstraintPtr newcons = rel->newConstraint((*it)->getFunction(), (*it)->getLb(), (*it)->getUb());
if (maxviol < knapviols[curknap]) {
maxviol = knapviols[curknap];
}
// add constraint to engine does not do anything.
// Thus, we add constraint to the relaxation and reload it to engine.
// e.addConstraint(newcons);
}
/*******************************************************************/
// Solution time of knapsack cover cuts added problem.
Double timemod = 0.0;
// Objective value after adding knapsack cover cuts.
Double endobj = 0.0;
// Gap closed by using knapsack cover cuts.
Double gapknap = 0.0;
/*******************************************************************/
if (violknapcuts >= 1) {
// Reload problem to engine.
// Check if we should reload the modified problem.
e.clear();
const Double * xupdated;
if (WARMSTART == 1) {
// Set initial point as the solution of root solution.
xupdated = sol->getPrimal();
rel->setInitialPoint(xupdated);
}
// Load the modified problem.
e.load(rel);
// warmstart continues.
if (WARMSTART == 1) {
// Before presolve, we set initial primal and
// dual solutions as the root solution.
SolutionPtr solupdated = (SolutionPtr) new Solution(initobj, xupdated, rel);
// Create new dual solution.
const Double * dualofvars = sol->getDualOfVars();
solupdated->setDualOfVars(dualofvars);
const Double * initdualofcons = sol->getDualOfCons();
UInt numconsupdated = rel->getNumCons();
Double * dualofcons = new Double[numconsupdated];
memcpy(dualofcons, initdualofcons, numcons*sizeof(Double));
for (UInt indexx = numcons; indexx < numconsupdated; ++indexx) {
dualofcons[indexx] = 0.0;
}
solupdated->setDualOfCons(dualofcons);
FilterWSPtr warmstart = (FilterWSPtr) new FilterSQPWarmStart();
warmstart->setPoint(solupdated);
e.loadFromWarmStart(warmstart);
delete [] dualofcons;
}
// Solution time after adding knapsack cover cuts to relaxation.
timer->start();
// Resolve the problem.
e.solve();
/*******************************************************************/
// Solution time of knapsack cover cuts added problem.
timemod = timer->query();
timer->stop();
// Objective value after adding knapsack cover cuts.
endobj = e.getSolutionValue();
// Gap closed by using knapsack cover cuts.
gapknap = (endobj-initobj)/fabs(initobj) * 100;
/*******************************************************************/
} else {
/*******************************************************************/
// Solution time of knapsack cover cuts added problem.
timemod = timeinit;
// Objective value after adding knapsack cover cuts.
endobj = initobj;
// Gap closed by using knapsack cover cuts.
gapknap = 0.0;
/*******************************************************************/
}
std::cout << "Objective value of relaxation after adding cuts: "
<< endobj << std::endl;
cout << pname << " " << numvars << " " << numcons << " " << numlin
<< " " << numknap
<< " " << numknapcov << " " << knaps << " " << totalcuts
<< " " << numknapcuts << " " << violknapcuts
<< std::fixed << std::setprecision(2)
<< " " << initobj << " " << endobj
<< " " << gapknap << " " << timeinit << " " << timecut
<< " " << timemod << endl;
if (numknap >= 1) {
// Save output data.
output << pname << " " << numvars << " " << numcons << " " << numlin
<< " " << numknap
<< " " << numknapcov << " " << knaps << " " << totalcuts
<< " " << numknapcuts << " " << violknapcuts
<< std::fixed << std::setprecision(2)
<< " " << initobj << " " << endobj
<< " " << gapknap << " " << timeinit << " " << timecut
<< " " << timemod << endl;
}
delete iface;
delete [] argv2;
}
output.close();
input.close();
return 0;
}
//! main routine implementing outer approximation
int main(int argc, char** argv)
{
//! Declaration of Variables ============================================
//! interface to AMPL (NULL):
MINOTAUR_AMPL::AMPLInterfacePtr iface = MINOTAUR_AMPL::AMPLInterfacePtr();
MINOTAUR_AMPL::JacobianPtr jPtr; //! Jacobian read from AMPL
MINOTAUR_AMPL::HessianOfLagPtr hPtr; //! Hessian read from AMPL
//! environment, timers, options:
EnvPtr env = (EnvPtr) new Environment();
TimerFactory *tFactory = new TimerFactory();
Timer *timer=tFactory->getTimer();
OptionDBPtr options; //! AMPL and MINOTAUR options
//! problem pointers (MINLP, NLP, MILP):
ProblemPtr minlp; //! MINLP instance to be solved
ProblemPtr milp; //! MILP master problem
//! solver pointers, including status
FilterSQPEngine e(env); //! NLP engine: FilterSQP
EngineStatus status;
//! pointers to manipulate variables & constraints
VariablePtr v, objVar; //! variable pointer (objective)
ObjectivePtr objFun; //! remember objective function pt.
//! local variables
Double *x, *xsoln;
Double *lobnd, *upbnd;
Double objfLo = -INFINITY, objfUp = INFINITY, objfNLP, objfMIP;
Double tol = 1E-2;
Int feasibleNLP = 0, iterOA = 1, n;
// ======================================================================
//! start the timer
timer->start();
//! make sure solver is used correctly
if (argc < 2) {
usage();
return -1;
}
//! add AMPL options & flags to environment: flags (options without values)
options = env->getOptions();
add_ampl_flags(options);
env->readOptions(argc, argv); //! parse options
if (!checkUserOK(options,env)) goto CLEANUP; //! check if user needs help
//! read MINLP from AMPL & create Hessian/Jacobian for NLP solves
iface = (MINOTAUR_AMPL::AMPLInterfacePtr) new MINOTAUR_AMPL::AMPLInterface(env);
minlp = iface->readInstance(options->findString("problem_file")->getValue(),false);
jPtr = (MINOTAUR_AMPL::JacobianPtr) new MINOTAUR_AMPL::Jacobian(iface);
hPtr = (MINOTAUR_AMPL::HessianOfLagPtr) new MINOTAUR_AMPL::HessianOfLag(iface);
minlp->setJacobian(jPtr);
minlp->setHessian(hPtr);
//! Display number of constraints and variables, and MINLP problem
minlp->calculateSize();
n = minlp->getNumVars();
std::cout << "No. of vars, cons = " << minlp->getNumVars()
<< minlp->getNumCons() << std::endl;
std::cout << std::endl << "The MINLP problem is: " << std::endl;
minlp->write(std::cout);
//! load the MINLP into the NLP solver (FilterSQP)
e.load(minlp);
//! get initial point & save original bounds
x = new Double[n];
xsoln = new Double[n];
lobnd = new Double[n];
upbnd = new Double[n];
std::copy(iface->getInitialPoint(),iface->getInitialPoint()+n,x);
for (VariableConstIterator i=minlp->varsBegin(); i!=minlp->varsEnd(); ++i) {
v = *i;
lobnd[v->getId()] = v->getLb();
upbnd[v->getId()] = v->getUb();
}
//! initialize the MILP master problem by copying variables & linear c/s
milp = (ProblemPtr) new Problem();
objFun = minlp->getObjective();
objVar = VariablePtr();
initMaster(minlp, milp, objVar, objFun, x);
while ((objfLo <= objfUp)&&(iterOA<4)){
std::cout << "Iteration " << iterOA << std::endl
<< "===============" << std::endl << std::endl;
//! set-up and solve NLP(y) with fixed integers
solveNLP(minlp, e, x, objfNLP, feasibleNLP, n);
std::cout << "Solved NLP " << iterOA << " objective = " << objfNLP
<< " NLPfeasible = " << feasibleNLP << std::endl;
if (feasibleNLP && (objfNLP-tol < objfUp)) {
objfUp = objfNLP - tol;
std::copy(x,x+n,xsoln);
}
//! update MILP master problem by adding outer approximations
updateMaster(minlp, milp, objVar, objFun, objfUp, x, n);
//! solve MILP master problem
solveMaster(env, milp, x, &objfMIP, n);
objfLo = objfMIP;
iterOA = iterOA + 1;
} // end while (objfLo <= objfUp)
//! output final result & timing
std::cout << std::endl << "END outer-approximation: f(x) = " << objfUp
<< " time used = " << timer->query() << std::endl;
CLEANUP:
//! free storage
delete timer;
delete tFactory;
if (minlp) {
minlp->clear();
delete [] x;
delete [] xsoln;
delete [] lobnd;
delete [] upbnd;
}
if (milp) {
milp->clear();
}
return 0;
} // end outer approximation main