/usr/include/opengm/inference/lpgurobi.hxx is in libopengm-dev 2.3.6+20160905-1.
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#ifndef OPENGM_LP_GURPBI_HXX
#define OPENGM_LP_GURPBI_HXX
#include <vector>
#include <string>
#include <iostream>
#include <fstream>
#include <stdexcept>
#include <typeinfo>
#include "gurobi_c++.h"
#include "opengm/datastructures/marray/marray.hxx"
#include "opengm/opengm.hxx"
#include "opengm/operations/adder.hxx"
#include "opengm/operations/minimizer.hxx"
#include "opengm/operations/maximizer.hxx"
#include "opengm/inference/inference.hxx"
#include "opengm/inference/auxiliary/lpdef.hxx"
#include "opengm/inference/visitors/visitors.hxx"
namespace opengm {
/// \brief Optimization by Linear Programming (LP) or Integer LP using Guroi\n\n
///http://www.gurobi.com
///
/// The optimization problem is reformulated as an LP or ILP.
/// For the LP, a first order local polytope approximation of the
/// marginal polytope is used, i.e. the affine instead of the convex
/// hull.
///
/// Gurobi is a commercial product that is
/// free for accadamical use.
///
/// \ingroup inference
template<class GM, class ACC>
class LPGurobi : public Inference<GM, ACC>, public LPDef {
public:
typedef ACC AccumulationType;
typedef ACC AccumulatorType;
typedef GM GraphicalModelType;
OPENGM_GM_TYPE_TYPEDEFS;
typedef visitors::VerboseVisitor<LPGurobi<GM, ACC> > VerboseVisitorType;
typedef visitors::TimingVisitor<LPGurobi<GM, ACC> > TimingVisitorType;
typedef visitors::EmptyVisitor< LPGurobi<GM, ACC> > EmptyVisitorType;
template<class _GM>
struct RebindGm{
typedef LPGurobi<_GM, ACC> type;
};
template<class _GM,class _ACC>
struct RebindGmAndAcc{
typedef LPGurobi<_GM, _ACC > type;
};
class Parameter {
public:
bool integerConstraint_;// ILP=true, 1order-LP=false
int numberOfThreads_; // number of threads (0=autosect)
bool verbose_; // switch on/off verbode mode
double cutUp_; // upper cutoff
double epOpt_; // Optimality tolerance
double epMrk_; // Markowitz tolerance
double epRHS_; // Feasibility Tolerance
double epInt_; // amount by which an integer variable can differ from an integer
double epAGap_; // Absolute MIP gap tolerance
double epGap_; // Relative MIP gap tolerance
double workMem_; // maximal ammount of memory in MB used for workspace
double treeMemoryLimit_; // maximal ammount of memory in MB used for treee
double timeLimit_; // maximal time in seconds the solver has
int probeingLevel_;
//int coverCutLevel_;
//int disjunctiverCutLevel_;
//int cliqueCutLevel_;
//int MIRCutLevel_;
//int presolveLevel_;
LP_SOLVER rootAlg_;
LP_SOLVER nodeAlg_;
MIP_EMPHASIS mipFocus_;
LP_PRESOLVE presolve_;
MIP_CUT cutLevel_; // Determines whether or not to cuts for the problem and how aggressively (will be overruled by specific ones).
MIP_CUT cliqueCutLevel_; // Determines whether or not to generate clique cuts for the problem and how aggressively.
MIP_CUT coverCutLevel_; // Determines whether or not to generate cover cuts for the problem and how aggressively.
MIP_CUT gubCutLevel_; // Determines whether or not to generate generalized upper bound (GUB) cuts for the problem and how aggressively.
MIP_CUT mirCutLevel_; // Determines whether or not mixed integer rounding (MIR) cuts should be generated for the problem and how aggressively.
MIP_CUT iboundCutLevel_; // Determines whether or not to generate implied bound cuts for the problem and how aggressively.
MIP_CUT flowcoverCutLevel_; //Determines whether or not to generate flow cover cuts for the problem and how aggressively.
MIP_CUT flowpathCutLevel_; //Determines whether or not to generate flow path cuts for the problem and how aggressively.
MIP_CUT disjunctCutLevel_; // Determines whether or not to generate disjunctive cuts for the problem and how aggressively.
MIP_CUT gomoryCutLevel_; // Determines whether or not to generate gomory fractional cuts for the problem and how aggressively.
template<class P>
Parameter(const P & p )
:
integerConstraint_(p.integerConstraint_),
numberOfThreads_(p.numberOfThreads_),
verbose_(p.verbose_),
cutUp_(p.cutUp_),
epOpt_(p.epOpt_),
epMrk_(p.epMrk_),
epRHS_(p.epRHS_),
epInt_(p.epInt_),
epAGap_(p.epAGap_),
epGap_(p.epGap_),
workMem_(p.workMem_),
treeMemoryLimit_(p.treeMemoryLimit_),
timeLimit_(p.timeLimit_),
probeingLevel_(p.probeingLevel_),
rootAlg_(p.rootAlg_),
nodeAlg_(p.nodeAlg_),
mipFocus_(p.mipFocus_),
presolve_(p.presolve_),
cutLevel_(p.cutLevel_),
cliqueCutLevel_(p.cliqueCutLevel_),
coverCutLevel_(p.coverCutLevel_),
gubCutLevel_(p.gubCutLevel_),
mirCutLevel_(p.mirCutLevel_),
iboundCutLevel_(p.iboundCutLevel_),
flowcoverCutLevel_(p.flowcoverCutLevel_),
flowpathCutLevel_(p.flowpathCutLevel_),
disjunctCutLevel_(p.disjunctCutLevel_),
gomoryCutLevel_(p.gomoryCutLevel_)
{
}
/// constructor
/// \param cutUp upper cutoff - assume that: min_x f(x) <= cutUp
/// \param epGap relative stopping criterion: |bestnode-bestinteger| / (1e-10 + |bestinteger|) <= epGap
Parameter
(
int numberOfThreads = 0
)
: numberOfThreads_(numberOfThreads),
//integerConstraint_(false),
verbose_(false),
workMem_(128.0),
treeMemoryLimit_(1e+75),
timeLimit_(1e+75),
probeingLevel_(0),
//coverCutLevel_(0),
//disjunctiverCutLevel_(0),
//cliqueCutLevel_(0),
//MIRCutLevel_(0),
//presolveLevel_(-1),
rootAlg_(LP_SOLVER_AUTO),
nodeAlg_(LP_SOLVER_AUTO),
mipFocus_(MIP_EMPHASIS_BALANCED),
presolve_(LP_PRESOLVE_AUTO),
cutLevel_(MIP_CUT_AUTO),
cliqueCutLevel_(MIP_CUT_AUTO),
coverCutLevel_(MIP_CUT_AUTO),
gubCutLevel_(MIP_CUT_AUTO),
mirCutLevel_(MIP_CUT_AUTO),
iboundCutLevel_(MIP_CUT_AUTO),
flowcoverCutLevel_(MIP_CUT_AUTO),
flowpathCutLevel_(MIP_CUT_AUTO),
disjunctCutLevel_(MIP_CUT_AUTO),
gomoryCutLevel_(MIP_CUT_AUTO)
{
numberOfThreads_ = numberOfThreads;
integerConstraint_ = false;
LPDef lpdef;
cutUp_ = lpdef.default_cutUp_;
epOpt_ = lpdef.default_epOpt_;
epMrk_ = lpdef.default_epMrk_;
epRHS_ = lpdef.default_epRHS_;
epInt_ = lpdef.default_epInt_;
epAGap_= lpdef.default_epAGap_;
epGap_ = lpdef.default_epGap_;
};
int getCutLevel(MIP_CUT cl){
switch(cl){
case MIP_CUT_AUTO:
return -1;
case MIP_CUT_OFF:
return 0;
case MIP_CUT_ON:
return 1;
case MIP_CUT_AGGRESSIVE:
return 2;
case MIP_CUT_VERYAGGRESSIVE:
return 3;
}
return -1;
};
};
LPGurobi(const GraphicalModelType&, const Parameter& = Parameter());
~LPGurobi();
virtual std::string name() const
{ return "LPGurobi"; }
const GraphicalModelType& graphicalModel() const;
virtual InferenceTermination infer();
template<class VisitorType>
InferenceTermination infer(VisitorType&);
virtual InferenceTermination arg(std::vector<LabelType>&, const size_t = 1) const;
virtual InferenceTermination args(std::vector<std::vector<LabelType> >&) const
{ return UNKNOWN; };
void variable(const size_t, IndependentFactorType& out) const;
void factorVariable(const size_t, IndependentFactorType& out) const;
typename GM::ValueType bound() const;
typename GM::ValueType value() const;
size_t lpNodeVi(const IndexType variableIndex,const LabelType label)const;
size_t lpFactorVi(const IndexType factorIndex,const size_t labelingIndex)const;
template<class LABELING_ITERATOR>
size_t lpFactorVi(const IndexType factorIndex,LABELING_ITERATOR labelingBegin,LABELING_ITERATOR labelingEnd)const;
template<class LPVariableIndexIterator, class CoefficientIterator>
void addConstraint(LPVariableIndexIterator, LPVariableIndexIterator, CoefficientIterator,const ValueType&, const ValueType&, const char * name=0);
void writeModelToDisk(const std::string & filename)const{
try {
if( filename.size()!=0)
model_->write(filename);
}
catch(GRBException e) {
std::cout << "**Error code = " << e.getErrorCode() << "\n";
std::cout << e.getMessage() <<"\n";
throw opengm::RuntimeError( e.getMessage() );
}
catch(...) {
std::cout << "Exception during write" <<"\n";
throw opengm::RuntimeError( "Exception during write" );
}
}
private:
void updateIfDirty();
const GraphicalModelType& gm_;
Parameter param_;
std::vector<size_t> idNodesBegin_;
std::vector<size_t> idFactorsBegin_;
std::vector<std::vector<size_t> > unaryFactors_;
bool inferenceStarted_;
bool dirty_;
std::vector<double> lpArg_;
std::vector<LabelType> arg_;
size_t nLpVar_;
// gurobi members
GRBEnv * env_ ;
GRBModel * model_;
GRBVar * vars_;
//
ValueType bound_;
ValueType value_;
};
template<class GM, class ACC>
LPGurobi<GM, ACC>::LPGurobi
(
const GraphicalModelType& gm,
const Parameter& para
)
: gm_(gm),
param_(para),
idNodesBegin_(gm_.numberOfVariables()),
idFactorsBegin_(gm_.numberOfFactors()),
unaryFactors_(gm_.numberOfVariables()),
inferenceStarted_(false),
dirty_(false),
lpArg_(),
arg_(gm_.numberOfVariables(),0),
nLpVar_(0),
env_(),
model_(),
vars_(),
bound_(),
value_()
{
ACC::neutral(value_);
ACC::ineutral(bound_);
//std::cout<<"setup basic env\n";
try {
env_ = new GRBEnv();
env_->set(GRB_IntParam_LogToConsole,int(param_.verbose_));
// Root Algorithm
switch(param_.nodeAlg_) {
case LP_SOLVER_AUTO:
env_->set(GRB_IntParam_NodeMethod,1);
break;
case LP_SOLVER_PRIMAL_SIMPLEX:
env_->set(GRB_IntParam_NodeMethod,0);
break;
case LP_SOLVER_DUAL_SIMPLEX:
env_->set(GRB_IntParam_NodeMethod,1);
break;
case LP_SOLVER_NETWORK_SIMPLEX:
throw RuntimeError("Gurobi does not support Network Simplex");
break;
case LP_SOLVER_BARRIER:
env_->set(GRB_IntParam_NodeMethod,2);
break;
case LP_SOLVER_SIFTING:
throw RuntimeError("Gurobi does not support Sifting");
break;
case LP_SOLVER_CONCURRENT:
throw RuntimeError("Gurobi does not concurrent solvers");
break;
}
// Node Algorithm
switch(param_.rootAlg_) {
case LP_SOLVER_AUTO:
env_->set(GRB_IntParam_Method,-1);
break;
case LP_SOLVER_PRIMAL_SIMPLEX:
env_->set(GRB_IntParam_Method,0);
break;
case LP_SOLVER_DUAL_SIMPLEX:
env_->set(GRB_IntParam_Method,1);
break;
case LP_SOLVER_NETWORK_SIMPLEX:
throw RuntimeError("Gurobi does not support Network Simplex");
break;
case LP_SOLVER_BARRIER:
env_->set(GRB_IntParam_Method,2);
break;
case LP_SOLVER_SIFTING:
env_->set(GRB_IntParam_Method,1);
env_->set(GRB_IntParam_SiftMethod,1);
break;
case LP_SOLVER_CONCURRENT:
env_->set(GRB_IntParam_Method,4);
break;
}
// presolve
switch(param_.presolve_) {
case LP_PRESOLVE_AUTO:
env_->set(GRB_IntParam_Presolve,-1);
break;
case LP_PRESOLVE_OFF:
env_->set(GRB_IntParam_Presolve,0);
break;
case LP_PRESOLVE_CONSERVATIVE:
env_->set(GRB_IntParam_Presolve,1);
break;
case LP_PRESOLVE_AGGRESSIVE:
env_->set(GRB_IntParam_Presolve,2);
break;
}
// MIP FOCUS
switch(param_.mipFocus_) {
case MIP_EMPHASIS_BALANCED:
env_->set(GRB_IntParam_MIPFocus,0);
break;
case MIP_EMPHASIS_FEASIBILITY:
env_->set(GRB_IntParam_MIPFocus,1);
break;
case MIP_EMPHASIS_OPTIMALITY:
env_->set(GRB_IntParam_MIPFocus,2);
break;
case MIP_EMPHASIS_BESTBOUND:
env_->set(GRB_IntParam_MIPFocus,3);
break;
case MIP_EMPHASIS_HIDDENFEAS:
throw RuntimeError("Gurobi does not support hidden feasibility as MIP-focus");
break;
}
// tolarance settings
env_->set(GRB_DoubleParam_Cutoff ,param_.cutUp_); // Optimality Tolerance
env_->set(GRB_DoubleParam_OptimalityTol ,param_.epOpt_); // Optimality Tolerance
env_->set(GRB_DoubleParam_IntFeasTol ,param_.epInt_); // amount by which an integer variable can differ from an integer
env_->set(GRB_DoubleParam_MIPGapAbs ,param_.epAGap_); // Absolute MIP gap tolerance
env_->set(GRB_DoubleParam_MIPGap ,param_.epGap_); // Relative MIP gap tolerance
env_->set(GRB_DoubleParam_FeasibilityTol,param_.epRHS_);
env_->set(GRB_DoubleParam_MarkowitzTol ,param_.epMrk_);
// set hints
// CutUp is missing http://www.gurobi.com/resources/switching-to-gurobi/switching-from-cplex#setting
// memory settings
// -missing
// time limit
env_->set(GRB_DoubleParam_TimeLimit ,param_.timeLimit_); // time limit
// threadding
if(param_.numberOfThreads_!=0)
env_->set(GRB_IntParam_Threads ,param_.numberOfThreads_); // threads
// tuning
// *Probe missing
// *DisjCuts missing
if(param_.cutLevel_ != MIP_CUT_DEFAULT)
env_->set(GRB_IntParam_Cuts ,param_.getCutLevel(param_.cutLevel_));
if(param_.cliqueCutLevel_ != MIP_CUT_DEFAULT)
env_->set(GRB_IntParam_CliqueCuts ,param_.getCutLevel(param_.cliqueCutLevel_));
if(param_.coverCutLevel_ != MIP_CUT_DEFAULT)
env_->set(GRB_IntParam_CoverCuts ,param_.getCutLevel(param_.coverCutLevel_));
if(param_.gubCutLevel_ != MIP_CUT_DEFAULT)
env_->set(GRB_IntParam_GUBCoverCuts ,param_.getCutLevel(param_.gubCutLevel_));
if(param_.mirCutLevel_ != MIP_CUT_DEFAULT)
env_->set(GRB_IntParam_MIRCuts ,param_.getCutLevel(param_.mirCutLevel_));
if(param_.iboundCutLevel_ != MIP_CUT_DEFAULT)
env_->set(GRB_IntParam_ImpliedCuts ,param_.getCutLevel(param_.iboundCutLevel_));
if(param_.flowcoverCutLevel_ != MIP_CUT_DEFAULT)
env_->set(GRB_IntParam_FlowCoverCuts ,param_.getCutLevel(param_.flowcoverCutLevel_));
if(param_.flowpathCutLevel_ != MIP_CUT_DEFAULT)
env_->set(GRB_IntParam_FlowPathCuts ,param_.getCutLevel(param_.flowpathCutLevel_));
// *DisjCuts missing
// *Gomory missing
model_ = new GRBModel(*env_);
}
catch(GRBException e) {
std::cout << "Error code = " << e.getErrorCode() << "\n";
std::cout << e.getMessage() <<"\n";
throw opengm::RuntimeError( e.getMessage() );
} catch(...) {
std::cout << "Exception during construction of gurobi solver" <<"\n";
throw opengm::RuntimeError( "Exception during construction of gurobi solver" );
}
if(typeid(OperatorType) != typeid(opengm::Adder)) {
throw RuntimeError("This implementation does only supports Min-Plus-Semiring");
}
//std::cout<<"enumerate stuff\n";
param_ = para;
idNodesBegin_.resize(gm_.numberOfVariables());
unaryFactors_.resize(gm_.numberOfVariables());
idFactorsBegin_.resize(gm_.numberOfFactors());
// temporal variables
size_t numberOfElements = 0;
size_t numberOfVariableElements = 0;
size_t numberOfFactorElements = 0;
size_t maxLabel = 0 ;
size_t maxFacSize = 0;
// enumerate variables
size_t idCounter = 0;
for(size_t node = 0; node < gm_.numberOfVariables(); ++node) {
numberOfVariableElements += gm_.numberOfLabels(node);
maxLabel=std::max(size_t(gm_.numberOfLabels(node)),maxLabel);
idNodesBegin_[node] = idCounter;
idCounter += gm_.numberOfLabels(node);
}
// enumerate factors
for(size_t f = 0; f < gm_.numberOfFactors(); ++f) {
if(gm_[f].numberOfVariables() == 1) {
size_t node = gm_[f].variableIndex(0);
unaryFactors_[node].push_back(f);
idFactorsBegin_[f] = idNodesBegin_[node];
}
else {
idFactorsBegin_[f] = idCounter;
idCounter += gm_[f].size();
maxFacSize=std::max(size_t(gm_[f].size()),maxFacSize);
numberOfFactorElements += gm_[f].size();
}
}
numberOfElements = numberOfVariableElements + numberOfFactorElements;
nLpVar_=numberOfElements; // refactor me
if(typeid(ACC) == typeid(opengm::Minimizer)) {
}
else {
throw RuntimeError("This implementation does only support Minimizer or Maximizer accumulators");
}
//std::cout<<"fill obj ptrs \n";
lpArg_.resize(nLpVar_);
std::vector<double> lb(numberOfElements,0.0);
std::vector<double> ub(numberOfElements,1.0);
std::vector<double> obj(numberOfElements);
std::vector<char> vtype(numberOfElements,GRB_CONTINUOUS);
// set variables and objective
if(param_.integerConstraint_) {
std::fill(vtype.begin(),vtype.begin()+numberOfVariableElements,GRB_BINARY);
}
for(size_t node = 0; node < gm_.numberOfVariables(); ++node) {
for(size_t i = 0; i < gm_.numberOfLabels(node); ++i) {
ValueType t = 0;
for(size_t n=0; n<unaryFactors_[node].size();++n) {
t += gm_[unaryFactors_[node][n]](&i);
}
obj[idNodesBegin_[node]+i] = t;
}
}
for(size_t f = 0; f < gm_.numberOfFactors(); ++f) {
if(gm_[f].numberOfVariables() == 2) {
size_t index[2];
size_t counter = idFactorsBegin_[f];
for(index[1]=0; index[1]<gm_[f].numberOfLabels(1);++index[1])
for(index[0]=0; index[0]<gm_[f].numberOfLabels(0);++index[0])
obj[counter++] = gm_[f](index);
}
else if(gm_[f].numberOfVariables() == 3) {
size_t index[3];
size_t counter = idFactorsBegin_[f] ;
for(index[2]=0; index[2]<gm_[f].numberOfLabels(2);++index[2])
for(index[1]=0; index[1]<gm_[f].numberOfLabels(1);++index[1])
for(index[0]=0; index[0]<gm_[f].numberOfLabels(0);++index[0])
obj[counter++] = gm_[f](index);
}
else if(gm_[f].numberOfVariables() == 4) {
size_t index[4];
size_t counter = idFactorsBegin_[f];
for(index[3]=0; index[3]<gm_[f].numberOfLabels(3);++index[3])
for(index[2]=0; index[2]<gm_[f].numberOfLabels(2);++index[2])
for(index[1]=0; index[1]<gm_[f].numberOfLabels(1);++index[1])
for(index[0]=0; index[0]<gm_[f].numberOfLabels(0);++index[0])
obj[counter++] = gm_[f](index);
}
else if(gm_[f].numberOfVariables() > 4) {
size_t counter = idFactorsBegin_[f];
std::vector<size_t> coordinate(gm_[f].numberOfVariables());
marray::Marray<bool> temp(gm_[f].shapeBegin(), gm_[f].shapeEnd());
for(marray::Marray<bool>::iterator mit = temp.begin(); mit != temp.end(); ++mit) {
mit.coordinate(coordinate.begin());
obj[counter++] = gm_[f](coordinate.begin());
}
}
}
//std::cout<<"add obj ptrs \n";
try {
// add all variables at once with an allready setup objective
vars_ = model_->addVars(&lb[0],&ub[0],&obj[0],&vtype[0],NULL,numberOfElements);
//integrate new variales
model_->update();
}
catch(GRBException e) {
std::cout << "**Error code = " << e.getErrorCode() << "\n";
std::cout << e.getMessage() <<"\n";
throw opengm::RuntimeError( e.getMessage() );
} catch(...) {
std::cout << "Exception during construction of gurobi model" <<"\n";
throw opengm::RuntimeError( "Exception during construction of gurobi model" );
}
//std::cout<<"count constr \n";
// count the needed constraints
size_t constraintCounter = 0;
// \sum_i \mu_i = 1
for(size_t node = 0; node < gm_.numberOfVariables(); ++node) {
++constraintCounter;
}
// \sum_i \mu_{f;i_1,...,i_n} - \mu{b;j}= 0
for(size_t f = 0; f < gm_.numberOfFactors(); ++f) {
if(gm_[f].numberOfVariables() > 1) {
for(size_t n = 0; n < gm_[f].numberOfVariables(); ++n) {
size_t node = gm_[f].variableIndex(n);
for(size_t i = 0; i < gm_.numberOfLabels(node); ++i) {
++constraintCounter;
}
}
}
}
std::vector<GRBLinExpr> lhsExprs(constraintCounter);
std::vector<char> sense(constraintCounter,GRB_EQUAL);
std::vector<double> rhsVals(constraintCounter,0.0);
std::vector<std::string> names(constraintCounter,std::string());
std::fill(rhsVals.begin(),rhsVals.begin()+gm_.numberOfVariables(),1.0);
//std::cout<<"setup constr \n";
// set constraints
constraintCounter = 0;
// \sum_i \mu_i = 1
const size_t buffferSize = std::max(maxLabel,size_t(maxFacSize+1));
std::vector<GRBVar> localVars(buffferSize);
std::vector<double> localVal(buffferSize,1.0);
for(size_t node = 0; node < gm_.numberOfVariables(); ++node) {
for(size_t i = 0; i < gm_.numberOfLabels(node); ++i) {
localVars[i]=vars_[idNodesBegin_[node]+i];
}
lhsExprs[constraintCounter].addTerms(&localVal[0],&localVars[0],gm_.numberOfLabels(node));
++constraintCounter;
}
localVal[0]=-1.0;
// \sum_i \mu_{f;i_1,...,i_n} - \mu{b;j}= 0
for(size_t f = 0; f < gm_.numberOfFactors(); ++f) {
if(gm_[f].numberOfVariables() > 1) {
marray::Marray<size_t> temp(gm_[f].shapeBegin(), gm_[f].shapeEnd());
size_t counter = idFactorsBegin_[f];
for(marray::Marray<size_t>::iterator mit = temp.begin(); mit != temp.end(); ++mit) {
*mit = counter++;
}
for(size_t n = 0; n < gm_[f].numberOfVariables(); ++n) {
size_t node = gm_[f].variableIndex(n);
for(size_t i = 0; i < gm_.numberOfLabels(node); ++i) {
//c_.add(IloRange(env_, 0, 0));
//c_[constraintCounter].setLinearCoef(x_[idNodesBegin_[node]+i], -1);
//double mone =-1.0;
//lhsExprs[constraintCounter].addTerms(&mone,&vars_[idNodesBegin_[node]+i],1);
size_t localCounter=1;
localVars[0]=vars_[idNodesBegin_[node]+i];
marray::View<size_t> view = temp.boundView(n, i);
for(marray::View<size_t>::iterator vit = view.begin(); vit != view.end(); ++vit) {
//c_[constraintCounter].setLinearCoef(x_[*vit], 1);
//double one =1.0;
//lhsExprs[constraintCounter].addTerms(&one,&vars_[*vit],1);
localVars[localCounter]=vars_[*vit];
++localCounter;
}
lhsExprs[constraintCounter].addTerms(&localVal[0],&localVars[0],localCounter);
++constraintCounter;
}
}
}
}
try {
//std::cout<<"add constr \n";
// add all constraints at once to the model
GRBConstr* constr = model_->addConstrs(&lhsExprs[0],&sense[0],&rhsVals[0],&names[0],constraintCounter);
//std::cout<<"done\n";
}
catch(GRBException e) {
std::cout << "**Error code = " << e.getErrorCode() << "\n";
std::cout << e.getMessage() <<"\n";
throw opengm::RuntimeError( e.getMessage() );
} catch(...) {
std::cout << "Exception during adding constring to gurobi model" <<"\n";
throw opengm::RuntimeError( "Exception during adding constring to gurobi model" );
}
// test if it help for write model to file
model_->update();
}
template <class GM, class ACC>
InferenceTermination
LPGurobi<GM, ACC>::infer() {
EmptyVisitorType v;
return infer(v);
}
template<class GM, class ACC>
template<class VisitorType>
InferenceTermination
LPGurobi<GM, ACC>::infer
(
VisitorType& visitor
) {
updateIfDirty();
visitor.begin(*this);
inferenceStarted_ = true;
try {
model_->optimize();
if(param_.integerConstraint_){
bound_ = model_->get(GRB_DoubleAttr_ObjBound);
}
else{
bound_ = model_->get(GRB_DoubleAttr_ObjVal);
}
//std::cout << "Bound: " <<bound_ << "\n";
for(size_t lpvi=0;lpvi<nLpVar_;++lpvi){
lpArg_[lpvi]=vars_[lpvi].get(GRB_DoubleAttr_X);
//td::cout<<"lpvi "<<lpvi<<" "<<lpArg_[lpvi]<<"\n";
}
}
catch(GRBException e) {
std::cout << "Error code = " << e.getErrorCode() << "\n";
std::cout << e.getMessage() <<"\n";
} catch(...) {
std::cout << "Exception during optimization" <<"\n";
}
visitor.end(*this);
return NORMAL;
}
template <class GM, class ACC>
LPGurobi<GM, ACC>::~LPGurobi() {
delete model_;
delete env_;
}
template <class GM, class ACC>
inline InferenceTermination
LPGurobi<GM, ACC>::arg
(
std::vector<typename LPGurobi<GM, ACC>::LabelType>& x,
const size_t N
) const {
x.resize(gm_.numberOfVariables());
if(inferenceStarted_) {
for(size_t node = 0; node < gm_.numberOfVariables(); ++node) {
ValueType value = lpArg_[idNodesBegin_[node]];
size_t state = 0;
for(size_t i = 1; i < gm_.numberOfLabels(node); ++i) {
if(lpArg_[idNodesBegin_[node]+i] > value) {
value = lpArg_[idNodesBegin_[node]+i];
state = i;
}
}
x[node] = state;
}
return NORMAL;
}
else{
for(size_t node = 0; node < gm_.numberOfVariables(); ++node) {
x[node] = 0;
}
return UNKNOWN;
}
}
template <class GM, class ACC>
void LPGurobi<GM, ACC>::variable
(
const size_t nodeId,
IndependentFactorType& out
) const {
size_t var[] = {nodeId};
size_t shape[] = {gm_.numberOfLabels(nodeId)};
out.assign(var, var + 1, shape, shape + 1);
for(size_t i = 0; i < gm_.numberOfLabels(nodeId); ++i) {
out(i) = lpArg_[idNodesBegin_[nodeId]+i];
}
//return UNKNOWN;
}
template <class GM, class ACC>
void LPGurobi<GM, ACC>::factorVariable
(
const size_t factorId,
IndependentFactorType& out
) const {
std::vector<size_t> var(gm_[factorId].numberOfVariables());
std::vector<size_t> shape(gm_[factorId].numberOfVariables());
for(size_t i = 0; i < gm_[factorId].numberOfVariables(); ++i) {
var[i] = gm_[factorId].variableIndex(i);
shape[i] = gm_[factorId].shape(i);
}
out.assign(var.begin(), var.end(), shape.begin(), shape.end());
if(gm_[factorId].numberOfVariables() == 1) {
size_t nodeId = gm_[factorId].variableIndex(0);
marginal(nodeId, out);
}
else {
size_t c = 0;
for(size_t n = idFactorsBegin_[factorId]; n<idFactorsBegin_[factorId]+gm_[factorId].size(); ++n) {
out(c++) = lpArg_[n];
}
}
//return UNKNOWN;
}
template<class GM, class ACC>
inline const typename LPGurobi<GM, ACC>::GraphicalModelType&
LPGurobi<GM, ACC>::graphicalModel() const
{
return gm_;
}
template<class GM, class ACC>
typename GM::ValueType LPGurobi<GM, ACC>::value() const {
std::vector<LabelType> states;
arg(states);
return gm_.evaluate(states);
}
template<class GM, class ACC>
typename GM::ValueType LPGurobi<GM, ACC>::bound() const {
if(param_.integerConstraint_) {
return bound_;
}
else{
return bound_;
}
}
template <class GM, class ACC>
inline size_t
LPGurobi<GM, ACC>::lpNodeVi
(
const typename LPGurobi<GM, ACC>::IndexType variableIndex,
const typename LPGurobi<GM, ACC>::LabelType label
)const{
OPENGM_ASSERT(variableIndex<gm_.numberOfVariables());
OPENGM_ASSERT(label<gm_.numberOfLabels(variableIndex));
return idNodesBegin_[variableIndex]+label;
}
template <class GM, class ACC>
inline size_t
LPGurobi<GM, ACC>::lpFactorVi
(
const typename LPGurobi<GM, ACC>::IndexType factorIndex,
const size_t labelingIndex
)const{
OPENGM_ASSERT(factorIndex<gm_.numberOfFactors());
OPENGM_ASSERT(labelingIndex<gm_[factorIndex].size());
return idFactorsBegin_[factorIndex]+labelingIndex;
}
template <class GM, class ACC>
template<class LABELING_ITERATOR>
inline size_t
LPGurobi<GM, ACC>::lpFactorVi
(
const typename LPGurobi<GM, ACC>::IndexType factorIndex,
LABELING_ITERATOR labelingBegin,
LABELING_ITERATOR labelingEnd
)const{
OPENGM_ASSERT(factorIndex<gm_.numberOfFactors());
OPENGM_ASSERT(std::distance(labelingBegin,labelingEnd)==gm_[factorIndex].numberOfVariables());
const size_t numVar=gm_[factorIndex].numberOfVariables();
size_t labelingIndex=labelingBegin[0];
size_t strides=gm_[factorIndex].numberOfLabels(0);
for(size_t vi=1;vi<numVar;++vi){
OPENGM_ASSERT(labelingBegin[vi]<gm_[factorIndex].numberOfLabels(vi));
labelingIndex+=strides*labelingBegin[vi];
strides*=gm_[factorIndex].numberOfLabels(vi);
}
return idFactorsBegin_[factorIndex]+labelingIndex;
}
/// \brief add constraint
/// \param viBegin iterator to the beginning of a sequence of variable indices
/// \param viEnd iterator to the end of a sequence of variable indices
/// \param coefficient iterator to the beginning of a sequence of coefficients
/// \param lowerBound lower bound
/// \param upperBound upper bound
///
/// variable indices refer to variables of the LP that is set up
/// in the constructor of LPGurobi (NOT to the variables of the
/// graphical model).
///
template<class GM, class ACC>
template<class LPVariableIndexIterator, class CoefficientIterator>
inline void LPGurobi<GM, ACC>::addConstraint(
LPVariableIndexIterator viBegin,
LPVariableIndexIterator viEnd,
CoefficientIterator coefficient,
const ValueType& lowerBound,
const ValueType& upperBound,
const char * name
) {
// construct linear constraint expression
GRBLinExpr expr;
while(viBegin != viEnd) {
expr += vars_[*viBegin] * (*coefficient);
++viBegin;
++coefficient;
}
// add constraints for upper and lower bound
model_->addConstr(expr, GRB_LESS_EQUAL, upperBound, name);
model_->addConstr(expr, GRB_GREATER_EQUAL, lowerBound, name);
// Gurobi needs a model update after adding a constraint
dirty_ = true;
}
template<class GM, class ACC>
inline void LPGurobi<GM, ACC>::updateIfDirty()
{
if(dirty_) {
model_->update();
dirty_ = false;
}
}
} // end namespace opengm
#endif
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