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Adapted MA model checker to new LP solver interface (LRA computation). 

Former-commit-id: b23b72c851
tempestpy_adaptions
dehnert 11 years ago
parent
29d8111991
commit
d80586b4aa
2 changed files with 33 additions and 33 deletions
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  1. 14
      src/modelchecker/csl/SparseMarkovAutomatonCslModelChecker.cpp
  2. 52
      src/modelchecker/csl/SparseMarkovAutomatonCslModelChecker.h

14
src/modelchecker/csl/SparseMarkovAutomatonCslModelChecker.cpp
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@ -1,8 +1,8 @@
// #include "src/modelchecker/csl/SparseMarkovAutomatonCslModelChecker.h"
#include "src/modelchecker/csl/SparseMarkovAutomatonCslModelChecker.h"
//bool SparseMarkovAutomatonCslModelCheckerOptionsRegistered = storm::settings::Settings::registerNewModule([] (storm::settings::Settings* instance) -> bool {
//
// instance->addOption(storm::settings::OptionBuilder("GmmxxLinearEquationSolver", "digiprecision", "", "Precision used for iterative solving of linear equation systems").addArgument(storm::settings::ArgumentBuilder::createDoubleArgument("precision value", "Precision").setDefaultValueDouble(1e-4).addValidationFunctionDouble(storm::settings::ArgumentValidators::doubleRangeValidatorExcluding(0.0, 1.0)).build()).build());
//
// return true;
//});
bool SparseMarkovAutomatonCslModelCheckerOptionsRegistered = storm::settings::Settings::registerNewModule([] (storm::settings::Settings* instance) -> bool {
instance->addOption(storm::settings::OptionBuilder("GmmxxLinearEquationSolver", "digiprecision", "", "Precision used for iterative solving of linear equation systems").addArgument(storm::settings::ArgumentBuilder::createDoubleArgument("precision value", "Precision").setDefaultValueDouble(1e-4).addValidationFunctionDouble(storm::settings::ArgumentValidators::doubleRangeValidatorExcluding(0.0, 1.0)).build()).build());
return true;
});

52
src/modelchecker/csl/SparseMarkovAutomatonCslModelChecker.h
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@ -431,58 +431,58 @@ namespace storm {
solver->setModelSense(min ? storm::solver::LpSolver::ModelSense::Maximize : storm::solver::LpSolver::ModelSense::Minimize);
// First, we need to create the variables for the problem.
std::map<uint_fast64_t, uint_fast64_t> stateToVariableIndexMap;
std::map<uint_fast64_t, std::string> stateToVariableNameMap;
for (auto const& stateChoicesPair : mec) {
stateToVariableIndexMap[stateChoicesPair.first] = solver->createContinuousVariable("x" + std::to_string(stateChoicesPair.first), storm::solver::LpSolver::UNBOUNDED, 0, 0, 0);
std::string variableName = "x" + std::to_string(stateChoicesPair.first);
stateToVariableNameMap[stateChoicesPair.first] = variableName;
solver->addUnboundedContinuousVariable(variableName);
}
uint_fast64_t lraValueVariableIndex = solver->createContinuousVariable("k", storm::solver::LpSolver::UNBOUNDED, 0, 0, 1);
solver->addUnboundedContinuousVariable("k", 1);
solver->update();
// Now we encode the problem as constraints.
std::vector<uint_fast64_t> variables;
std::vector<double> coefficients;
for (auto const& stateChoicesPair : mec) {
uint_fast64_t state = stateChoicesPair.first;
// Now, based on the type of the state, create a suitable constraint.
if (markovianStates.get(state)) {
variables.clear();
coefficients.clear();
variables.push_back(stateToVariableIndexMap.at(state));
coefficients.push_back(1);
storm::expressions::Expression constraint = storm::expressions::Expression::createDoubleVariable(stateToVariableNameMap.at(state));
for (auto element : transitionMatrix.getRow(nondeterministicChoiceIndices[state])) {
variables.push_back(stateToVariableIndexMap.at(element.getColumn()));
coefficients.push_back(-element.getValue());
constraint = constraint - storm::expressions::Expression::createDoubleVariable(stateToVariableNameMap.at(element.getColumn()));
}
variables.push_back(lraValueVariableIndex);
coefficients.push_back(storm::utility::constantOne<ValueType>() / exitRates[state]);
solver->addConstraint("state" + std::to_string(state), variables, coefficients, min ? storm::solver::LpSolver::LESS_EQUAL : storm::solver::LpSolver::GREATER_EQUAL, goalStates.get(state) ? storm::utility::constantOne<ValueType>() / exitRates[state] : storm::utility::constantZero<ValueType>());
constraint = constraint + storm::expressions::Expression::createDoubleLiteral(storm::utility::constantOne<ValueType>() / exitRates[state]) * storm::expressions::Expression::createDoubleVariable("k");
storm::expressions::Expression rightHandSide = goalStates.get(state) ? storm::expressions::Expression::createDoubleLiteral(storm::utility::constantOne<ValueType>() / exitRates[state]) : storm::expressions::Expression::createDoubleLiteral(storm::utility::constantZero<ValueType>());
if (min) {
constraint = constraint <= rightHandSide;
} else {
constraint = constraint >= rightHandSide;
}
solver->addConstraint("state" + std::to_string(state), constraint);
} else {
// For probabilistic states, we want to add the constraint x_s <= sum P(s, a, s') * x_s' where a is the current action
// and the sum ranges over all states s'.
for (auto choice : stateChoicesPair.second) {
variables.clear();
coefficients.clear();
variables.push_back(stateToVariableIndexMap.at(state));
coefficients.push_back(1);
storm::expressions::Expression constraint = storm::expressions::Expression::createDoubleVariable(stateToVariableNameMap.at(state));
for (auto element : transitionMatrix.getRow(choice)) {
variables.push_back(stateToVariableIndexMap.at(element.getColumn()));
coefficients.push_back(-element.getValue());
constraint = constraint - storm::expressions::Expression::createDoubleVariable(stateToVariableNameMap.at(element.getColumn()));
}
solver->addConstraint("state" + std::to_string(state), variables, coefficients, min ? storm::solver::LpSolver::LESS_EQUAL : storm::solver::LpSolver::GREATER_EQUAL, storm::utility::constantZero<ValueType>());
storm::expressions::Expression rightHandSide = storm::expressions::Expression::createDoubleLiteral(storm::utility::constantZero<ValueType>());
if (min) {
constraint = constraint <= rightHandSide;
} else {
constraint = constraint >= rightHandSide;
}
solver->addConstraint("state" + std::to_string(state), constraint);
}
}
}
solver->optimize();
return solver->getContinuousValue(lraValueVariableIndex);
return solver->getContinuousValue("k");
}
/*!

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