void QLDSolver::updateInequalityEquations()
{
int p = static_cast<int>(_m);
for (size_t i=0; i<_inequalityConstraints.size(); ++i)
{
LinearConstraint* cstr = _inequalityConstraints[i];
int dim = cstr->getDimension();
if(cstr->getType() == CSTR_LOWER_AND_GREATER)
dim *= 2;
// XXX http://eigen.tuxfamily.org/api/TopicFunctionTakingEigenTypes.html#TopicPlainFunctionsFailing
Eigen::Block<Eigen::Map<Eigen::MatrixXd> > _A_block = _A.block(p, 0, dim, n());
Eigen::VectorBlock<Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, 1> > > _b_segment = _b.segment(p, dim);
Eigen::VectorXd v;
utils::convert(*cstr, findMapping(cstr->getVariable()), CSTR_PLUS_GREATER, _A_block, _b_segment, v);
p += dim;
}
}
std::pair<int64,int64> LinearPropagator::computeMinMax(const LinearConstraint& l)
{
auto views = l.getViews();
std::pair<int64,int64> minmax(0,0);
for (auto i : views)
{
auto r = vs_.getCurrentRestrictor(i);
minmax.first += r.lower();
minmax.second += r.upper();
}
return minmax;
}
void
BundleCollector::addHyperplane(std::vector<double>& a, double b) {
/*
<w,a> + b ≤ ξ
<=>
<w,a> - ξ ≤ -b
*/
unsigned int dims = a.size();
LinearConstraint constraint;
for (unsigned int i = 0; i < dims; i++)
constraint.setCoefficient(i, a[i]);
constraint.setCoefficient(dims, -1.0);
constraint.setRelation(LessEqual);
constraint.setValue(-b);
_constraints->add(constraint);
_constraintAdded();
}
int64 LinearLiteralPropagator::computeMin(const LinearConstraint& l, itervec& clause)
{
auto& views = l.getViews();
int64 min(0);
for (auto& i : views)
{
auto r = vs_.getVariableStorage().getCurrentRestrictor(i);
assert(!r.isEmpty());
min += r.lower();
clause.emplace_back(r.begin());
}
return min;
}
std::pair<int64,int64> LinearLiteralPropagator::computeMinMax(const LinearConstraint& l, LinearConstraintClause::itervec& clause)
{
auto views = l.getViews();
std::pair<int64,int64> minmax(0,0);
for (auto i : views)
{
auto r = vs_.getVariableStorage().getCurrentRestrictor(i);
assert(!r.isEmpty());
minmax.first += r.lower();
minmax.second += r.upper();
clause.emplace_back(r.begin());
}
return minmax;
}
bool QPProblemFromLCCQP::Solve(VectorXd& sol)
{
LinearConstraint ultimateConstraint = mLCConstraint->GetLinearConstraint(mIsEqualityOnZ);
// LinearConstraint ultimateConstraint;
QPSolver solver;
solver.SetObjective(*mObjective);
ultimateConstraint.Union(*mLinearConstraint);
//ultimateConstraint = *mLinearConstraint;
//EigenSerializer::SaveToFile("Linear.txt", ultimateConstraint.GetLhs());
//EigenSerializer::SaveToFile("LCC.txt", ultimateConstraint.GetLhs());
solver.SetConstraints(ultimateConstraint);
int numLCConstraints = mLCConstraint->GetNumConstraints();
mIsMinimizationSucceed = solver.Solve(mSol, SM_Cone);
mInequalityOnBoundIdx.clear();
if (mIsMinimizationSucceed)
{
mMinValue = mObjective->OutputObjectiveFuncValues(mSol, false);
sol = mSol;
bool constraintSatisfaction = ultimateConstraint.CheckValidation(mSol);
const vector<ConstraintValidationT>& consSatisfactionTypes = ultimateConstraint.GetConstraintSatisfactionType();
for (int i = 0; i < 2 * numLCConstraints; ++i)
{
CHECK(i == consSatisfactionTypes[i].first);
CHECK(consSatisfactionTypes[i].second != CST_UpperBounded);
if (consSatisfactionTypes[i].second == CST_LowerBounded)
{
mInequalityOnBoundIdx.push_back(i % numLCConstraints);
}
}
mGradient = mObjective->EvaluateGradient(mSol);
}
return mIsMinimizationSucceed;
}
void
MinimalImpactTEDWriter::write(std::string filename) {
updateInputs();
// Create the internal pipeline
createPipeline();
// Remove the old file
/*if( std::remove( filename ) != 0 ) {
LOG_DEBUG(minimalImpactTEDlog) << "Old file to output minimal impact TED approximation sucessfully deleted." << std::endl;
}
else {
LOG_DEBUG(minimalImpactTEDlog) << "Could not delete old file to output minimal impact TED approximation." << std::endl;
}*/
// Get a vector with all gold standard segments.
const std::vector<boost::shared_ptr<Segment> > goldStandard = _goldStandard->getSegments();
int constant = 0;
LOG_DEBUG(minimalImpactTEDlog) << "in write function." << std::endl;
// Loop through variables
std::set<unsigned int> variables = _problemConfiguration->getVariables();
for ( unsigned int varNum = 0 ; varNum < variables.size() ; varNum++ ) {
// Introduce constraints that flip the segment corresponding to the ith variable
// compared to the goldstandard.
// Is the segment that corresponds to the variable part of the gold standard?
unsigned int segmentId = _problemConfiguration->getSegmentId(varNum);
bool isContained = false;
foreach (boost::shared_ptr<Segment> s, goldStandard) {
if (s->getId() == segmentId) {
isContained = true;
}
}
LinearConstraint constraint;
constraint.setRelation(Equal);
if (isContained == true) {
// Force segment to not be used in reconstruction
constraint.setCoefficient(varNum,1.0);
constraint.setValue(0);
}
else {
// Force segment to be used in reconstruction
constraint.setCoefficient(varNum,1.0);
constraint.setValue(1);
}
_linearConstraints->add(constraint);
_linearSolver->setInput("linear constraints",_linearConstraints);
pipeline::Value<Errors> errors = _teDistance->getOutput("errors");
unsigned int sumErrors = errors->getNumSplits() + errors->getNumMerges() + errors->getNumFalsePositives() + errors->getNumFalseNegatives();
int sumErrorsInt = (int) sumErrors;
if (isContained == true) {
// Forced segment to not be part of the reconstruction.
// This resulted in a number of errors that are going to be stored in the constant.
// To make net 0 errors when the variable is on, minus the number of errors will be written to the file.
writeToFile(filename, -sumErrorsInt, varNum, false);
constant += sumErrorsInt;
}
else {
// Forced segment to be part of the reconstruction.
// This resulted in a number of errors that are going to be written to the file.
writeToFile(filename, sumErrorsInt, varNum, false);
}
// Remove constraint
_linearConstraints->removeLastConstraint();
}
// Write constant to file
writeToFile(filename, constant, 0, true);
}
double
IlpSolver::min_surface(const Parameters& parameters) {
std::size_t num_vars = _num_nodes*_num_levels;
LOG_DEBUG(ilpsolverlog) << "creating objective for " << num_vars << " binary variables" << std::endl;
LinearObjective objective(num_vars);
LOG_DEBUG(ilpsolverlog) << "setting objective coefficients" << std::endl;
for (GraphType::NodeIt n(_graph); n != lemon::INVALID; ++n) {
double sum = 0;
for (int l = 0; l < _num_levels; l++) {
std::size_t var_num = _graph.id(n)*_num_levels + l;
double costs = _level_costs[n][l];
double accumulated_costs = costs - sum;
sum += accumulated_costs;
objective.setCoefficient(var_num, accumulated_costs);
}
}
LinearConstraints constraints;
// pick at least lowest level
LOG_DEBUG(ilpsolverlog) << "adding indicator constraints" << std::endl;
for (GraphType::NodeIt n(_graph); n != lemon::INVALID; ++n) {
std::size_t var_num = _graph.id(n)*_num_levels;
LinearConstraint indicator;
indicator.setCoefficient(var_num, 1.0);
indicator.setRelation(Equal);
indicator.setValue(1.0);
constraints.add(indicator);
}
// column inclusion constraints
LOG_DEBUG(ilpsolverlog) << "adding column inclusion constraints" << std::endl;
for (GraphType::NodeIt n(_graph); n != lemon::INVALID; ++n) {
for (int l = 1; l < _num_levels; l++) {
std::size_t upper_var_num = _graph.id(n)*_num_levels + l;
std::size_t lower_var_num = _graph.id(n)*_num_levels + l - 1;
LinearConstraint inclusion;
inclusion.setCoefficient(upper_var_num, 1.0);
inclusion.setCoefficient(lower_var_num, -1.0);
inclusion.setRelation(LessEqual);
inclusion.setValue(0.0);
constraints.add(inclusion);
}
}
// gradient constraints
LOG_DEBUG(ilpsolverlog) << "adding gradient constraints" << std::endl;
for (GraphType::EdgeIt e(_graph); e != lemon::INVALID; ++e) {
GraphType::Node u = _graph.u(e);
GraphType::Node v = _graph.v(e);
for (auto& p : { std::make_pair(u, v), std::make_pair(v, u) }) {
GraphType::Node u_ = p.first;
GraphType::Node v_ = p.second;
for (int l = _max_gradients[e]; l < _num_levels; l++) {
std::size_t upper_var_num = _graph.id(u_)*_num_levels + l;
std::size_t lower_var_num = _graph.id(v_)*_num_levels + l - _max_gradients[e];
LinearConstraint inclusion;
inclusion.setCoefficient(upper_var_num, 1.0);
inclusion.setCoefficient(lower_var_num, -1.0);
inclusion.setRelation(LessEqual);
inclusion.setValue(0.0);
constraints.add(inclusion);
}
}
}
if (parameters.enforce_zero_minimum) {
LOG_USER(ilpsolverlog) << "enforcing minima of zero" << std::endl;
if (parameters.num_neighbors < 0)
UTIL_THROW_EXCEPTION(
UsageError,
"if 'enforce_zero_minimum' is set, 'num_neighbors' has to be set, too.");
// for each indicator between 1 and _num_levels - 1: if top indicator t
// is 0, one of the neighbors n ∈ N must be zero, too:
//
// t=0 ⇒ Σn<|N|
//
// Σn - t ≤ |N| - 1 if t=0, one of neighbors has to be 0
// if t=1, constraint always true
for (GraphType::NodeIt n(_graph); n != lemon::INVALID; ++n) {
for (int l = 1; l < _num_levels - 1; l++) {
LinearConstraint zero_minimum;
// Σn
for (GraphType::IncEdgeIt e(_graph, n); e != lemon::INVALID; ++e) {
GraphType::Node nb = _graph.oppositeNode(n, e);
std::size_t nb_var_num = _graph.id(nb)*_num_levels + l;
zero_minimum.setCoefficient(nb_var_num, 1.0);
}
// -t
std::size_t t_var_num = _graph.id(n)*_num_levels + l + 1;
zero_minimum.setCoefficient(t_var_num, -1.0);
// ≤ |N| - 1
zero_minimum.setRelation(LessEqual);
zero_minimum.setValue(parameters.num_neighbors - 1);
constraints.add(zero_minimum);
}
}
}
SolverFactory factory;
_solver = std::unique_ptr<LinearSolverBackend>(factory.createLinearSolverBackend());
LOG_DEBUG(ilpsolverlog) << "initialize solver" << std::endl;
if (parameters.solve_relaxed_problem) {
_solver->initialize(num_vars, Continuous);
// we have to force values to be within 0 and 1
for (std::size_t i = 0; i < num_vars; i++) {
LinearConstraint lower_bound, upper_bound;
lower_bound.setCoefficient(i, 1.0);
lower_bound.setRelation(GreaterEqual);
lower_bound.setValue(0.0);
upper_bound.setCoefficient(i, 1.0);
upper_bound.setRelation(LessEqual);
upper_bound.setValue(1.0);
constraints.add(lower_bound);
constraints.add(upper_bound);
}
} else {
_solver->initialize(num_vars, Binary);
}
LOG_DEBUG(ilpsolverlog) << "setting objective" << std::endl;
_solver->setObjective(objective);
LOG_DEBUG(ilpsolverlog) << "setting setting constraints" << std::endl;
_solver->setConstraints(constraints);
LOG_ALL(ilpsolverlog) << objective << std::endl;
for (auto c : constraints)
LOG_ALL(ilpsolverlog) << c << std::endl;
LinearSolverBackend::Parameters solverParameters;
solverParameters.numThreads = parameters.num_threads;
solverParameters.verbose = parameters.verbose;
LOG_DEBUG(ilpsolverlog) << "solving" << std::endl;
std::string message;
if (!_solver->solve(_solution, message, solverParameters))
UTIL_THROW_EXCEPTION(
LinearSolverBackendException,
"linear program could not be solved: " << message);
LOG_ALL(ilpsolverlog) << _solution.getVector() << std::endl;
return _solution.getValue();
}
void
TolerantEditDistance::findBestCellLabels() {
pipeline::Value<LinearConstraints> constraints;
pipeline::Value<LinearSolverParameters> parameters;
// the default are binary variables
parameters->setVariableType(Binary);
// introduce indicators for each cell and each possible label of that cell
unsigned int var = 0;
for (unsigned int cellIndex = 0; cellIndex < _toleranceFunction->getCells()->size(); cellIndex++) {
cell_t& cell = (*_toleranceFunction->getCells())[cellIndex];
// first indicator variable for this cell
unsigned int begin = var;
// one variable for the default label
assignIndicatorVariable(var++, cellIndex, cell.getGroundTruthLabel(), cell.getReconstructionLabel());
// one variable for each alternative
foreach (float l, cell.getAlternativeLabels()) {
unsigned int ind = var++;
_alternativeIndicators.push_back(ind);
assignIndicatorVariable(ind, cellIndex, cell.getGroundTruthLabel(), l);
}
// last +1 indicator variable for this cell
unsigned int end = var;
// every cell needs to have a label
LinearConstraint constraint;
for (unsigned int i = begin; i < end; i++)
constraint.setCoefficient(i, 1.0);
constraint.setRelation(Equal);
constraint.setValue(1);
constraints->add(constraint);
}
_numIndicatorVars = var;
// labels can not disappear
foreach (float recLabel, _toleranceFunction->getReconstructionLabels()) {
LinearConstraint constraint;
foreach (unsigned int v, getIndicatorsByRec(recLabel))
constraint.setCoefficient(v, 1.0);
constraint.setRelation(GreaterEqual);
constraint.setValue(1);
constraints->add(constraint);
}
// introduce indicators for each match of ground truth label to
// reconstruction label
foreach (float gtLabel, _toleranceFunction->getGroundTruthLabels())
foreach (float recLabel, _toleranceFunction->getPossibleMatchesByGt(gtLabel))
assignMatchVariable(var++, gtLabel, recLabel);
// cell label selection activates match
foreach (float gtLabel, _toleranceFunction->getGroundTruthLabels()) {
foreach (float recLabel, _toleranceFunction->getPossibleMatchesByGt(gtLabel)) {
unsigned int matchVar = getMatchVariable(gtLabel, recLabel);
// no assignment of gtLabel to recLabel -> match is zero
LinearConstraint noMatchConstraint;
foreach (unsigned int v, getIndicatorsGtToRec(gtLabel, recLabel)) {
noMatchConstraint.setCoefficient(v, 1);
// at least one assignment of gtLabel to recLabel -> match is
// one
LinearConstraint matchConstraint;
matchConstraint.setCoefficient(matchVar, 1);
matchConstraint.setCoefficient(v, -1);
matchConstraint.setRelation(GreaterEqual);
matchConstraint.setValue(0);
constraints->add(matchConstraint);
}
noMatchConstraint.setCoefficient(matchVar, -1);
noMatchConstraint.setRelation(GreaterEqual);
noMatchConstraint.setValue(0);
constraints->add(noMatchConstraint);
}
}
