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Use acyclic solver in various Markov automata methods.

main
Tim Quatmann 5 years ago
parent
c83721066c
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99f2344da9
1 changed files with 50 additions and 54 deletions
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  1. 104
      src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.cpp

104
src/storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.cpp
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@ -32,6 +32,34 @@ namespace storm {
namespace modelchecker {
namespace helper {
template<typename ValueType>
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> setUpProbabilisticStatesSolver(storm::Environment& env, OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitions) {
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver;
// The min-max system has no end components as we assume non-zeno MAs.
if (transitions.getNonzeroEntryCount() > 0) {
storm::solver::GeneralMinMaxLinearEquationSolverFactory<ValueType> factory;
bool isAcyclic = !storm::utility::graph::hasCycle(transitions);
if (isAcyclic) {
env.solver().minMax().setMethod(storm::solver::MinMaxMethod::Acyclic);
}
solver = factory.create(env, transitions);
solver->setHasUniqueSolution(true); // Assume non-zeno MA
solver->setHasNoEndComponents(true); // assume non-zeno MA
solver->setLowerBound(storm::utility::zero<ValueType>());
solver->setUpperBound(storm::utility::one<ValueType>());
solver->setCachingEnabled(true);
solver->setRequirementsChecked(true);
auto req = solver->getRequirements(env, dir);
req.clearBounds();
req.clearUniqueSolution();
if (isAcyclic) {
req.clearAcyclic();
}
STORM_LOG_THROW(!req.hasEnabledCriticalRequirement(), storm::exceptions::UncheckedRequirementException, "The solver requirement " << req.getEnabledRequirementsAsString() << " has not been checked.");
}
return solver;
}
template<typename ValueType>
class UnifPlusHelper {
public:
@ -377,25 +405,6 @@ namespace storm {
}
}
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> setUpProbabilisticStatesSolver(storm::Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitions) const {
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver;
if (transitions.getNonzeroEntryCount() > 0) {
storm::solver::GeneralMinMaxLinearEquationSolverFactory<ValueType> factory;
solver = factory.create(env, transitions);
solver->setHasUniqueSolution(true); // Assume non-zeno MA
solver->setHasNoEndComponents(true); // assume non-zeno MA
solver->setLowerBound(storm::utility::zero<ValueType>());
solver->setUpperBound(storm::utility::one<ValueType>());
solver->setCachingEnabled(true);
solver->setRequirementsChecked(true);
auto req = solver->getRequirements(env, dir);
req.clearBounds();
req.clearUniqueSolution();
STORM_LOG_THROW(!req.hasEnabledCriticalRequirement(), storm::exceptions::UncheckedRequirementException, "The solver requirement " << req.getEnabledRequirementsAsString() << " has not been checked.");
}
return solver;
}
storm::storage::BitVector getProb0States(OptimizationDirection dir, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates) const {
if (dir == storm::solver::OptimizationDirection::Maximize) {
return storm::utility::graph::performProb0A(transitionMatrix.transpose(true), phiStates, psiStates);
@ -492,20 +501,11 @@ namespace storm {
}
}
// Check for requirements of the solver.
// The min-max system has no end components as we assume non-zeno MAs.
storm::solver::GeneralMinMaxLinearEquationSolverFactory<ValueType> minMaxLinearEquationSolverFactory;
storm::solver::MinMaxLinearEquationSolverRequirements requirements = minMaxLinearEquationSolverFactory.getRequirements(env, true, true, dir);
requirements.clearBounds();
STORM_LOG_THROW(!requirements.hasEnabledCriticalRequirement(), storm::exceptions::UncheckedRequirementException, "Solver requirements " + requirements.getEnabledRequirementsAsString() + " not checked.");
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> solver = minMaxLinearEquationSolverFactory.create(env, aProbabilistic);
solver->setHasUniqueSolution();
solver->setHasNoEndComponents();
solver->setBounds(storm::utility::zero<ValueType>(), storm::utility::one<ValueType>());
solver->setRequirementsChecked();
solver->setCachingEnabled(true);
// Create a solver object (only if there are actually transitions between probabilistic states)
auto solverEnv = env;
solverEnv.solver().setForceExact(true);
auto solver = setUpProbabilisticStatesSolver(solverEnv, dir, aProbabilistic);
// Perform the actual value iteration
// * loop until the step bound has been reached
// * in the loop:
@ -520,7 +520,11 @@ namespace storm {
storm::utility::vector::addVectors(bProbabilistic, bProbabilisticFixed, bProbabilistic);
// Now perform the inner value iteration for probabilistic states.
solver->solveEquations(env, dir, probabilisticNonGoalValues, bProbabilistic);
if (solver) {
solver->solveEquations(solverEnv, dir, probabilisticNonGoalValues, bProbabilistic);
} else {
storm::utility::vector::reduceVectorMinOrMax(dir, bProbabilistic, probabilisticNonGoalValues, aProbabilistic.getRowGroupIndices());
}
// (Re-)compute bMarkovian = bMarkovianFixed + aMarkovianToProbabilistic * vProbabilistic.
aMarkovianToProbabilistic.multiplyWithVector(probabilisticNonGoalValues, bMarkovian);
@ -543,7 +547,11 @@ namespace storm {
// After the loop, perform one more step of the value iteration for PS states.
aProbabilisticToMarkovian.multiplyWithVector(markovianNonGoalValues, bProbabilistic);
storm::utility::vector::addVectors(bProbabilistic, bProbabilisticFixed, bProbabilistic);
solver->solveEquations(env, dir, probabilisticNonGoalValues, bProbabilistic);
if (solver) {
solver->solveEquations(solverEnv, dir, probabilisticNonGoalValues, bProbabilistic);
} else {
storm::utility::vector::reduceVectorMinOrMax(dir, bProbabilistic, probabilisticNonGoalValues, aProbabilistic.getRowGroupIndices());
}
}
}
@ -1077,29 +1085,13 @@ namespace storm {
if (env.solver().isForceSoundness()) {
// To get correct results, the inner equation systems are solved exactly.
// TODO investigate how an error would propagate
solverEnv.solver().minMax().setMethod(storm::solver::MinMaxMethod::Topological);
solverEnv.solver().topological().setUnderlyingMinMaxMethod(storm::solver::MinMaxMethod::PolicyIteration);
solverEnv.solver().setLinearEquationSolverType(storm::solver::EquationSolverType::Topological);
solverEnv.solver().topological().setUnderlyingEquationSolverType(storm::solver::EquationSolverType::Eigen);
solverEnv.solver().eigen().setMethod(storm::solver::EigenLinearEquationSolverMethod::SparseLU);
solverEnv.solver().setForceExact(true);
}
x.resize(aProbabilistic.getRowGroupCount(), storm::utility::zero<ValueType>());
b = probabilisticChoiceRewards;
// Check for requirements of the solver.
// The solution is unique as we assume non-zeno MAs.
storm::solver::GeneralMinMaxLinearEquationSolverFactory<ValueType> minMaxLinearEquationSolverFactory;
storm::solver::MinMaxLinearEquationSolverRequirements requirements = minMaxLinearEquationSolverFactory.getRequirements(solverEnv, true, true, dir);
requirements.clearLowerBounds();
STORM_LOG_THROW(!requirements.hasEnabledCriticalRequirement(), storm::exceptions::UncheckedRequirementException, "Solver requirements " + requirements.getEnabledRequirementsAsString() + " not checked.");
solver = minMaxLinearEquationSolverFactory.create(solverEnv, std::move(aProbabilistic));
solver->setLowerBound(storm::utility::zero<ValueType>());
solver->setHasUniqueSolution(true);
solver->setHasNoEndComponents(true);
solver->setRequirementsChecked(true);
solver->setCachingEnabled(true);
solver = setUpProbabilisticStatesSolver(solverEnv, dir, aProbabilistic);
}
uint64_t iter = 0;
@ -1111,7 +1103,11 @@ namespace storm {
++iter;
// Compute the expected total rewards for the probabilistic states
if (hasProbabilisticStates) {
solver->solveEquations(solverEnv, dir, x, b);
if (solver) {
solver->solveEquations(solverEnv, dir, x, b);
} else {
storm::utility::vector::reduceVectorMinOrMax(dir, b, x, aProbabilistic.getRowGroupIndices());
}
}
// now compute the values for the markovian states. We also keep track of the maximal and minimal difference between two values (for convergence checking)
auto vIt = v.begin();

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