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NondeterministicLraHelper: Removed old ViHelper code.

tempestpy_adaptions
Tim Quatmann 4 years ago
parent
7e65e797fa
commit
f113ac7187
1 changed files with 18 additions and 533 deletions
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  1. 551
      src/storm/modelchecker/helper/infinitehorizon/SparseNondeterministicInfiniteHorizonHelper.cpp

551
src/storm/modelchecker/helper/infinitehorizon/SparseNondeterministicInfiniteHorizonHelper.cpp
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@ -1,10 +1,12 @@
#include "SparseNondeterministicInfiniteHorizonHelper.h"
#include "storm/modelchecker/helper/infinitehorizon/internal/LraViHelper.h"
#include "storm/solver/MinMaxLinearEquationSolver.h"
#include "storm/solver/LinearEquationSolver.h"
#include "storm/solver/Multiplier.h"
#include "storm/solver/LpSolver.h"
#include "storm/utility/graph.h"
#include "storm/utility/SignalHandler.h"
#include "storm/utility/solver.h"
#include "storm/utility/vector.h"
@ -84,9 +86,11 @@ namespace storm {
auto underlyingSolverEnvironment = env;
if (env.solver().isForceSoundness()) {
// For sound computations, the error in the MECS plus the error in the remaining system should not exceed the user defined precsion.
underlyingSolverEnvironment.solver().minMax().setPrecision(env.solver().lra().getPrecision() / storm::utility::convertNumber<storm::RationalNumber>(2));
storm::RationalNumber newPrecision = env.solver().lra().getPrecision() / storm::utility::convertNumber<storm::RationalNumber>(2);
underlyingSolverEnvironment.solver().minMax().setPrecision(newPrecision);
underlyingSolverEnvironment.solver().minMax().setRelativeTerminationCriterion(env.solver().lra().getRelativeTerminationCriterion());
underlyingSolverEnvironment.solver().lra().setPrecision(env.solver().lra().getPrecision() / storm::utility::convertNumber<storm::RationalNumber>(2));
underlyingSolverEnvironment.solver().setLinearEquationSolverPrecision(newPrecision, env.solver().lra().getRelativeTerminationCriterion());
underlyingSolverEnvironment.solver().lra().setPrecision(newPrecision);
}
// If requested, allocate memory for the choices made
@ -204,545 +208,26 @@ namespace storm {
}
}
/*!
* Abstract helper class that performs a single iteration of the value iteration method
*/
template <typename ValueType>
class LraViHelper {
public:
LraViHelper(storm::storage::MaximalEndComponent const& mec, storm::storage::SparseMatrix<ValueType> const& transitionMatrix) : _mec(mec), _transitionMatrix(transitionMatrix) {
// Intentionally left empty
}
virtual ~LraViHelper() = default;
/*!
* performs a single iteration step.
* If a choices vector is given, the optimal choices will be inserted at the appropriate states.
* Note that these choices will be inserted w.r.t. the original model states/choices, i.e. the size of the vector should match the state-count of the input model
* @return the current estimate of the LRA value
*/
virtual void iterate(Environment const& env, storm::solver::OptimizationDirection const& dir, std::vector<uint64_t>* choices = nullptr) = 0;
struct ConvergenceCheckResult {
bool isPrecisionAchieved;
ValueType currentValue;
};
/*!
* Checks whether the curently computed value achieves the desired precision
*/
virtual ConvergenceCheckResult checkConvergence(bool relative, ValueType precision) = 0;
/*!
* Must be called between two calls of iterate.
*/
virtual void prepareNextIteration(Environment const& env) = 0;
protected:
/*!
*
* @param xPrevious the 'old' values
* @param xCurrent the 'new' values
* @param threshold the threshold
* @param relative whether the relative difference should be considered
* @return The first component is true if the (relative) difference between the maximal and the minimal entry-wise change of the two value vectors is below or equal to the provided threshold.
* In this case, the second component is the average of the maximal and the minimal change.
* If the threshold is exceeded, the computation is aborted early and the second component is only an approximation of the averages.
*/
std::pair<bool, ValueType> checkMinMaxDiffBelowThreshold(std::vector<ValueType> const& xPrevious, std::vector<ValueType> const& xCurrent, ValueType const& threshold, bool relative) const {
STORM_LOG_ASSERT(xPrevious.size() == xCurrent.size(), "Unexpected Dimension Mismatch");
STORM_LOG_ASSERT(threshold > storm::utility::zero<ValueType>(), "Did not expect a non-positive threshold.");
auto x1It = xPrevious.begin();
auto x1Ite = xPrevious.end();
auto x2It = xCurrent.begin();
ValueType maxDiff = (*x2It - *x1It);
ValueType minDiff = maxDiff;
bool result = true;
// The difference between maxDiff and minDiff is zero at this point. Thus, it doesn't make sense to check the threshold now.
for (++x1It, ++x2It; x1It != x1Ite; ++x1It, ++x2It) {
ValueType diff = (*x2It - *x1It);
// Potentially update maxDiff or minDiff
bool skipCheck = false;
if (maxDiff < diff) {
maxDiff = diff;
} else if (minDiff > diff) {
minDiff = diff;
} else {
skipCheck = true;
}
// Check convergence
if (!skipCheck && (maxDiff - minDiff) > (relative ? (threshold * minDiff) : threshold)) {
result = false;
break;
}
}
ValueType avgDiff = (maxDiff + minDiff) / (storm::utility::convertNumber<ValueType>(2.0));
return {result, avgDiff};
}
storm::storage::MaximalEndComponent const& _mec;
storm::storage::SparseMatrix<ValueType> const& _transitionMatrix;
};
/*!
* Abstract helper class that performs a single iteration of the value iteration method for MDP
* @see Ashok et al.: Value Iteration for Long-Run Average Reward in Markov Decision Processes (CAV'17), https://doi.org/10.1007/978-3-319-63387-9_10
*/
template <typename ValueType>
class MdpLraViHelper : public LraViHelper<ValueType> {
public:
MdpLraViHelper(storm::storage::MaximalEndComponent const& mec, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, std::function<ValueType(uint64_t stateIndex)> const& stateRewardsGetter, std::function<ValueType(uint64_t globalChoiceIndex)> const& actionRewardsGetter, ValueType const& aperiodicFactor) : LraViHelper<ValueType>(mec, transitionMatrix), _x1(mec.size(), storm::utility::zero<ValueType>()), _x2(_x1), _x1IsCurrent(true) {
// We add a selfloop to each state (which is necessary for convergence)
// Very roughly, this selfloop avoids that the values can flip around like this: [1, 0] -> [0, 1] -> [1, 0] -> ...
ValueType selfLoopProb = aperiodicFactor;
// Introducing the selfloop also requires the rewards to be scaled by the following factor.
_scalingFactor = storm::utility::one<ValueType>() - selfLoopProb;
uint64_t numMecStates = this->_mec.size();
boost::container::flat_map<uint64_t, uint64_t> toSubModelStateMapping;
toSubModelStateMapping.reserve(numMecStates);
uint64_t currState = 0;
uint64_t numMecChoices = 0;
for (auto const& stateChoices : this->_mec) {
toSubModelStateMapping.emplace(stateChoices.first, currState);
++currState;
numMecChoices += stateChoices.second.size();
}
assert(currState == numMecStates);
// Get a transition matrix that only considers the states and choices within the MEC
storm::storage::SparseMatrixBuilder<ValueType> mecTransitionBuilder(numMecChoices, numMecStates, 0, true, true, numMecStates);
_choiceValues.reserve(numMecChoices);
uint64_t currRow = 0;
for (auto const& stateChoices : this->_mec) {
auto const& mecState = stateChoices.first;
auto const& mecChoices = stateChoices.second;
mecTransitionBuilder.newRowGroup(currRow);
for (auto const& choice : mecChoices) {
bool insertedDiagElement = false;
for (auto const& entry : this->_transitionMatrix.getRow(choice)) {
uint64_t column = toSubModelStateMapping[entry.getColumn()];
if (!insertedDiagElement && entry.getColumn() > mecState) {
mecTransitionBuilder.addNextValue(currRow, toSubModelStateMapping[mecState], selfLoopProb);
insertedDiagElement = true;
}
if (!insertedDiagElement && entry.getColumn() == mecState) {
mecTransitionBuilder.addNextValue(currRow, column, selfLoopProb + _scalingFactor * entry.getValue());
insertedDiagElement = true;
} else {
mecTransitionBuilder.addNextValue(currRow, column, _scalingFactor * entry.getValue());
}
}
if (!insertedDiagElement) {
mecTransitionBuilder.addNextValue(currRow, toSubModelStateMapping[mecState], selfLoopProb);
}
// Compute the rewards obtained for this choice
_choiceValues.push_back(_scalingFactor * (stateRewardsGetter(mecState) + actionRewardsGetter(choice)));
++currRow;
}
}
_mecTransitions = mecTransitionBuilder.build();
STORM_LOG_ASSERT(_mecTransitions.isProbabilistic(), "The MEC-Matrix is not probabilistic.");
STORM_LOG_ASSERT(_mecTransitions.getRowGroupCount() == _x1.size(), "Unexpected size mismatch for created matrix.");
STORM_LOG_ASSERT(_x1.size() == _x2.size(), "Unexpected size mismatch for created matrix.");
}
virtual void iterate(Environment const& env, storm::solver::OptimizationDirection const& dir, std::vector<uint64_t>* choices = nullptr) override {
// Initialize a multipler if it does not exist, yet
if (!_multiplier) {
_multiplier = storm::solver::MultiplierFactory<ValueType>().create(env, _mecTransitions);
}
if (choices == nullptr) {
// Perform a simple matrix-vector multiplication
_multiplier->multiplyAndReduce(env, dir, xCurrent(), &_choiceValues, xPrevious());
} else {
// Perform a simple matrix-vector multiplication but also keep track of the choices within the _mecTransitions
std::vector<uint64_t> mecChoices(_mecTransitions.getRowGroupCount());
_multiplier->multiplyAndReduce(env, dir, xCurrent(), &_choiceValues, xPrevious(), &mecChoices);
// Transform the local choices (within this mec) to global indices
uint64_t mecState = 0;
for (auto const& stateChoices : this->_mec) {
uint64_t mecChoice = mecChoices[mecState];
STORM_LOG_ASSERT(mecChoice < stateChoices.second.size(), "The selected choice does not seem to exist.");
uint64_t globalChoiceIndex = *(stateChoices.second.begin() + mecChoice);
(*choices)[stateChoices.first] = globalChoiceIndex - this->_transitionMatrix.getRowGroupIndices()[stateChoices.first];
++mecState;
}
}
// Swap current and previous x vectors
_x1IsCurrent = !_x1IsCurrent;
}
virtual typename LraViHelper<ValueType>::ConvergenceCheckResult checkConvergence(bool relative, ValueType precision) override {
typename LraViHelper<ValueType>::ConvergenceCheckResult res;
std::tie(res.isPrecisionAchieved, res.currentValue) = this->checkMinMaxDiffBelowThreshold(xPrevious(), xCurrent(), precision, relative);
res.currentValue /= _scalingFactor; // "Undo" the scaling of the rewards
return res;
}
virtual void prepareNextIteration(Environment const&) override {
// To avoid large (and numerically unstable) x-values, we substract a reference value.
ValueType referenceValue = xCurrent().front();
storm::utility::vector::applyPointwise<ValueType, ValueType>(xCurrent(), xCurrent(), [&referenceValue] (ValueType const& x_i) -> ValueType { return x_i - referenceValue; });
}
private:
std::vector<ValueType>& xCurrent() {
return _x1IsCurrent ? _x1 : _x2;
}
std::vector<ValueType>& xPrevious() {
return _x1IsCurrent ? _x2 : _x1;
}
storm::storage::SparseMatrix<ValueType> _mecTransitions;
std::vector<ValueType> _x1, _x2, _choiceValues;
bool _x1IsCurrent;
std::unique_ptr<storm::solver::Multiplier<ValueType>> _multiplier;
ValueType _scalingFactor;
};
/*!
* Abstract helper class that performs a single iteration of the value iteration method for MA
* @see Butkova, Wimmer, Hermanns: Long-Run Rewards for Markov Automata (TACAS'17), https://doi.org/10.1007/978-3-662-54580-5_11
*/
template <typename ValueType>
class MaLraViHelper : public LraViHelper<ValueType> {
public:
MaLraViHelper(storm::storage::MaximalEndComponent const& mec, storm::storage::SparseMatrix<ValueType> const& transitionMatrix, storm::storage::BitVector const& markovianStates, std::vector<ValueType> const& exitRates, std::function<ValueType(uint64_t stateIndex)> const& stateRewardsGetter, std::function<ValueType(uint64_t globalChoiceIndex)> const& actionRewardsGetter, ValueType const& aperiodicFactor) : LraViHelper<ValueType>(mec, transitionMatrix), _markovianStates(markovianStates), _Msx1IsCurrent(false) {
// Run through the Mec and collect some data:
// We consider two submodels, one consisting of the Markovian MEC states and one consisting of the probabilistic MEC states.
// For this, we create a state index map that point from state indices of the input model to indices of the corresponding submodel of that state.
boost::container::flat_map<uint64_t, uint64_t> toSubModelStateMapping;
// We also obtain state and choices counts of the two submodels
uint64_t numPsSubModelStates(0), numPsSubModelChoices(0);
uint64_t numMsSubModelStates(0); // The number of choices coincide
// We will need to uniformize the Markovian MEC states by introducing a selfloop.
// For this, we need to find a uniformization rate which will be a little higher (given by aperiodicFactor) than the maximum rate occurring in the MEC.
_uniformizationRate = storm::utility::zero<ValueType>();
// Now run over the MEC and collect the required data.
for (auto const& stateChoices : this->_mec) {
uint64_t const& mecState = stateChoices.first;
if (_markovianStates.get(mecState)) {
toSubModelStateMapping.emplace(mecState, numMsSubModelStates);
++numMsSubModelStates;
STORM_LOG_ASSERT(stateChoices.second.size() == 1, "Markovian state has multiple MEC choices.");
_uniformizationRate = std::max(_uniformizationRate, exitRates[mecState]);
} else {
toSubModelStateMapping.emplace(mecState, numPsSubModelStates);
++numPsSubModelStates;
numPsSubModelChoices += stateChoices.second.size();
}
}
assert(numPsSubModelStates + numMsSubModelStates == mec.size());
STORM_LOG_THROW(numMsSubModelStates > 0, storm::exceptions::InvalidOperationException, "Markov Automaton has Zeno behavior. Computation of Long Run Average values not supported.");
_hasProbabilisticStates = numPsSubModelStates > 0;
// We make sure that every Markovian state gets a selfloop to make the model aperiodic
_uniformizationRate *= storm::utility::one<ValueType>() + aperiodicFactor;
// Now build the Markovian and the Probabilistic submodels.
// In addition, we also need the transitions between the two.
storm::storage::SparseMatrixBuilder<ValueType> msTransitionsBuilder(numMsSubModelStates, numMsSubModelStates);
_MsChoiceValues.reserve(numMsSubModelStates);
storm::storage::SparseMatrixBuilder<ValueType> msToPsTransitionsBuilder, psTransitionsBuilder, psToMsTransitionsBuilder;
if (_hasProbabilisticStates) {
msToPsTransitionsBuilder = storm::storage::SparseMatrixBuilder<ValueType>(numMsSubModelStates, numPsSubModelStates);
psTransitionsBuilder = storm::storage::SparseMatrixBuilder<ValueType>(numPsSubModelChoices, numPsSubModelStates, 0, true, true, numPsSubModelStates);
psToMsTransitionsBuilder = storm::storage::SparseMatrixBuilder<ValueType>(numPsSubModelChoices, numMsSubModelStates, 0, true, true, numPsSubModelStates);
_PsChoiceValues.reserve(numPsSubModelChoices);
}
uint64_t currMsRow = 0;
uint64_t currPsRow = 0;
for (auto const& stateChoices : this->_mec) {
uint64_t const& mecState = stateChoices.first;
auto const& mecChoices = stateChoices.second;
if (!_hasProbabilisticStates || _markovianStates.get(mecState)) {
// The currently processed state is Markovian.
// We need to uniformize!
ValueType uniformizationFactor = exitRates[mecState] / _uniformizationRate;
ValueType selfLoopProb = storm::utility::one<ValueType>() - uniformizationFactor;
STORM_LOG_ASSERT(mecChoices.size() == 1, "Unexpected number of choices at Markovian state.");
for (auto const& mecChoice : mecChoices) {
bool insertedDiagElement = false;
for (auto const& entry : this->_transitionMatrix.getRow(mecChoice)) {
uint64_t subModelColumn = toSubModelStateMapping[entry.getColumn()];
if (!_hasProbabilisticStates || _markovianStates.get(entry.getColumn())) {
// We have a transition from a Markovian state to a Markovian state
STORM_LOG_ASSERT(subModelColumn < numMsSubModelStates, "Invalid state for Markovian submodel");
if (!insertedDiagElement && subModelColumn > currMsRow) {
// We passed the diagonal entry, so add it now before moving on to the next entry
msTransitionsBuilder.addNextValue(currMsRow, currMsRow, selfLoopProb);
insertedDiagElement = true;
}
if (!insertedDiagElement && subModelColumn == currMsRow) {
// The current entry is the diagonal (selfloop) entry
msTransitionsBuilder.addNextValue(currMsRow, subModelColumn, selfLoopProb + uniformizationFactor * entry.getValue());
insertedDiagElement = true;
} else {
// The diagonal element either has been inserted already or still lies in front
msTransitionsBuilder.addNextValue(currMsRow, subModelColumn, uniformizationFactor * entry.getValue());
}
} else {
// We have a transition from a Markovian to a probabilistic state
STORM_LOG_ASSERT(subModelColumn < numPsSubModelStates, "Invalid state for probabilistic submodel");
msToPsTransitionsBuilder.addNextValue(currMsRow, subModelColumn, uniformizationFactor * entry.getValue());
}
}
// If the diagonal entry for the MS matrix still has not been set, we do that now
if (!insertedDiagElement) {
msTransitionsBuilder.addNextValue(currMsRow, currMsRow, selfLoopProb);
}
// Compute the rewards obtained for this choice.
_MsChoiceValues.push_back(stateRewardsGetter(mecState) / _uniformizationRate + actionRewardsGetter(mecChoice) * exitRates[mecState] / _uniformizationRate);
++currMsRow;
}
} else {
// The currently processed state is probabilistic
psTransitionsBuilder.newRowGroup(currPsRow);
psToMsTransitionsBuilder.newRowGroup(currPsRow);
for (auto const& mecChoice : mecChoices) {
for (auto const& entry : this->_transitionMatrix.getRow(mecChoice)) {
uint64_t subModelColumn = toSubModelStateMapping[entry.getColumn()];
if (_markovianStates.get(entry.getColumn())) {
// We have a transition from a probabilistic state to a Markovian state
STORM_LOG_ASSERT(subModelColumn < numMsSubModelStates, "Invalid state for Markovian submodel");
psToMsTransitionsBuilder.addNextValue(currPsRow, subModelColumn, entry.getValue());
} else {
// We have a transition from a probabilistic to a probabilistic state
STORM_LOG_ASSERT(subModelColumn < numPsSubModelStates, "Invalid state for probabilistic submodel");
psTransitionsBuilder.addNextValue(currPsRow, subModelColumn, entry.getValue());
}
}
// Compute the rewards obtained for this choice.
// State rewards do not count here since no time passes in probabilistic states.
_PsChoiceValues.push_back(actionRewardsGetter(mecChoice));
++currPsRow;
}
}
}
_MsTransitions = msTransitionsBuilder.build();
if (_hasProbabilisticStates) {
_MsToPsTransitions = msToPsTransitionsBuilder.build();
_PsTransitions = psTransitionsBuilder.build();
_PsToMsTransitions = psToMsTransitionsBuilder.build();
}
}
void initializeIterations(Environment const& env, storm::solver::OptimizationDirection const& dir) {
_Msx1.resize(_MsTransitions.getRowGroupCount(), storm::utility::zero<ValueType>());
_Msx2 = _Msx1;
_MsMultiplier = storm::solver::MultiplierFactory<ValueType>().create(env, _MsTransitions);
if (_hasProbabilisticStates) {
if (_PsTransitions.getNonzeroEntryCount() > 0) {
// Set-up a solver for transitions within PS states
_PsSolverEnv = env;
if (env.solver().isForceSoundness()) {
// To get correct results, the inner equation systems are solved exactly.
// TODO investigate how an error would propagate
_PsSolverEnv.solver().setForceExact(true);
}
storm::solver::GeneralMinMaxLinearEquationSolverFactory<ValueType> factory;
bool isAcyclic = !storm::utility::graph::hasCycle(_PsTransitions);
if (isAcyclic) {
STORM_LOG_INFO("Probabilistic transitions are acyclic.");
_PsSolverEnv.solver().minMax().setMethod(storm::solver::MinMaxMethod::Acyclic);
}
_PsSolver = factory.create(_PsSolverEnv, _PsTransitions);
_PsSolver->setHasUniqueSolution(true); // Assume non-zeno MA
_PsSolver->setHasNoEndComponents(true); // assume non-zeno MA
_PsSolver->setCachingEnabled(true);
_PsSolver->setRequirementsChecked(true);
auto req = _PsSolver->getRequirements(_PsSolverEnv, dir);
req.clearUniqueSolution();
if (isAcyclic) {
req.clearAcyclic();
}
// Computing a priori lower/upper bounds is not particularly easy, as there might be selfloops with high probabilities
// Which accumulate a lot of reward. Moreover, the right-hand-side of the equation system changes dynamically.
STORM_LOG_THROW(!req.hasEnabledCriticalRequirement(), storm::exceptions::UnmetRequirementException, "The solver requirement " << req.getEnabledRequirementsAsString() << " has not been checked.");
}
// Set up multipliers for transitions connecting Markovian and probabilistic states
_MsToPsMultiplier = storm::solver::MultiplierFactory<ValueType>().create(env, _MsToPsTransitions);
_PsToMsMultiplier = storm::solver::MultiplierFactory<ValueType>().create(env, _PsToMsTransitions);
// Set-up vectors for storing intermediate results for PS states.
_Psx.resize(_PsTransitions.getRowGroupCount(), storm::utility::zero<ValueType>());
_Psb = _PsChoiceValues;
}
}
void setInputModelChoices(std::vector<uint64_t>& choices, std::vector<uint64_t> const& localMecChoices, bool setChoiceZeroToMarkovianStates) {
// Transform the local choices (within this mec) to choice indices for the input model
uint64_t mecState = 0;
for (auto const& stateChoices : this->_mec) {
if (setChoiceZeroToMarkovianStates && _markovianStates.get(stateChoices.first)) {
choices[stateChoices.first] = 0;
} else {
uint64_t mecChoice = localMecChoices[mecState];
STORM_LOG_ASSERT(mecChoice < stateChoices.second.size(), "The selected choice does not seem to exist.");
uint64_t globalChoiceIndex = *(stateChoices.second.begin() + mecChoice);
choices[stateChoices.first] = globalChoiceIndex - this->_transitionMatrix.getRowGroupIndices()[stateChoices.first];
++mecState;
}
}
STORM_LOG_ASSERT(mecState == localMecChoices.size(), "Did not traverse all mec states.");
}
virtual void iterate(Environment const& env, storm::solver::OptimizationDirection const& dir, std::vector<uint64_t>* choices = nullptr) override {
// Initialize value vectors, multiplers, and solver if this has not been done, yet
if (!_MsMultiplier) {
initializeIterations(env, dir);
}
// Compute new x values for the Markovian states
// Flip what is current and what is previous
_Msx1IsCurrent = !_Msx1IsCurrent;
// At this point, xPrevious() points to what has been computed in the previous call of iterate (initially, this is the 0-vector).
// The result of this computation will be stored in xCurrent()
// Compute the values obtained by a single uniformization step between Markovian states only
_MsMultiplier->multiply(env, xPrevious(), &_MsChoiceValues, xCurrent());
if (_hasProbabilisticStates) {
// Add the values obtained by taking a single uniformization step that leads to a Probabilistic state followed by arbitrarily many probabilistic steps.
// First compute the total values when taking arbitrarily many probabilistic transitions (in no time)
if (_PsSolver) {
// We might need to track the optimal choices.
if (choices == nullptr) {
_PsSolver->solveEquations(_PsSolverEnv, dir, _Psx, _Psb);
} else {
_PsSolver->setTrackScheduler();
_PsSolver->solveEquations(_PsSolverEnv, dir, _Psx, _Psb);
setInputModelChoices(*choices, _PsSolver->getSchedulerChoices(), true);
}
} else {
STORM_LOG_ASSERT(_PsTransitions.getNonzeroEntryCount() == 0, "If no solver was initialized, an empty matrix would have been expected.");
if (choices == nullptr) {
storm::utility::vector::reduceVectorMinOrMax(dir, _Psb, _Psx, _PsTransitions.getRowGroupIndices());
} else {
std::vector<uint64_t> psMecChoices(_PsTransitions.getRowGroupCount());
storm::utility::vector::reduceVectorMinOrMax(dir, _Psb, _Psx, _PsTransitions.getRowGroupIndices(), &psMecChoices);
setInputModelChoices(*choices, _PsSolver->getSchedulerChoices(), true);
}
}
// Now add the (weighted) values of the probabilistic states to the values of the Markovian states.
_MsToPsMultiplier->multiply(env, _Psx, &xCurrent(), xCurrent());
}
}
virtual typename LraViHelper<ValueType>::ConvergenceCheckResult checkConvergence(bool relative, ValueType precision) override {
typename LraViHelper<ValueType>::ConvergenceCheckResult res;
// All values are scaled according to the uniformizationRate.
// We need to 'revert' this scaling when computing the absolute precision.
// However, for relative precision, the scaling cancels out.
ValueType threshold = relative ? precision : ValueType(precision / _uniformizationRate);
std::tie(res.isPrecisionAchieved, res.currentValue) = this->checkMinMaxDiffBelowThreshold(xPrevious(), xCurrent(), threshold, relative);
res.currentValue *= _uniformizationRate; // "Undo" the scaling of the values
return res;
}
virtual void prepareNextIteration(Environment const& env) override {
// To avoid large (and numerically unstable) x-values, we substract a reference value.
ValueType referenceValue = xCurrent().front();
storm::utility::vector::applyPointwise<ValueType, ValueType>(xCurrent(), xCurrent(), [&referenceValue] (ValueType const& x_i) -> ValueType { return x_i - referenceValue; });
if (_hasProbabilisticStates) {
// Update the RHS of the equation system for the probabilistic states by taking the new values of Markovian states into account.
_PsToMsMultiplier->multiply(env, xCurrent(), &_PsChoiceValues, _Psb);
}
}
private:
std::vector<ValueType>& xCurrent() {
return _Msx1IsCurrent ? _Msx1 : _Msx2;
}
std::vector<ValueType>& xPrevious() {
return _Msx1IsCurrent ? _Msx2 : _Msx1;
}
storm::storage::BitVector const& _markovianStates;
bool _hasProbabilisticStates;
ValueType _uniformizationRate;
storm::storage::SparseMatrix<ValueType> _MsTransitions, _MsToPsTransitions, _PsTransitions, _PsToMsTransitions;
std::vector<ValueType> _Msx1, _Msx2, _MsChoiceValues;
bool _Msx1IsCurrent;
std::vector<ValueType> _Psx, _Psb, _PsChoiceValues;
std::unique_ptr<storm::solver::Multiplier<ValueType>> _MsMultiplier, _MsToPsMultiplier, _PsToMsMultiplier;
std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ValueType>> _PsSolver;
Environment _PsSolverEnv;
};
template <typename ValueType>
ValueType SparseNondeterministicInfiniteHorizonHelper<ValueType>::computeLraForMecVi(Environment const& env, std::function<ValueType(uint64_t stateIndex)> const& stateRewardsGetter, std::function<ValueType(uint64_t globalChoiceIndex)> const& actionRewardsGetter, storm::storage::MaximalEndComponent const& mec) {
// Collect some parameters of the computation.
// Collect some parameters of the computation
ValueType aperiodicFactor = storm::utility::convertNumber<ValueType>(env.solver().lra().getAperiodicFactor());
ValueType precision = storm::utility::convertNumber<ValueType>(env.solver().lra().getPrecision()) / aperiodicFactor;
bool relative = env.solver().lra().getRelativeTerminationCriterion();
boost::optional<uint64_t> maxIter;
if (env.solver().lra().isMaximalIterationCountSet()) {
maxIter = env.solver().lra().getMaximalIterationCount();
std::vector<uint64_t>* optimalChoices = nullptr;
if (isProduceSchedulerSet()) {
optimalChoices = &_producedOptimalChoices.get();
}
auto dir = this->getOptimizationDirection();
// Create an object for the iterations
std::shared_ptr<LraViHelper<ValueType>> iterationHelper;
// Now create a helper and perform the algorithm
if (isContinuousTime()) {
iterationHelper = std::make_shared<MaLraViHelper<ValueType>>(mec, _transitionMatrix, *_markovianStates, *_exitRates, stateRewardsGetter, actionRewardsGetter, aperiodicFactor);
} else {
iterationHelper = std::make_shared<MdpLraViHelper<ValueType>>(mec, _transitionMatrix, stateRewardsGetter, actionRewardsGetter, aperiodicFactor);
}
// start the iterations
ValueType result = storm::utility::zero<ValueType>();
uint64_t iter = 0;
while (!maxIter.is_initialized() || iter < maxIter.get()) {
++iter;
iterationHelper->iterate(env, dir);
// Check if we are done
auto convergenceCheckResult = iterationHelper->checkConvergence(relative, precision);
result = convergenceCheckResult.currentValue;
if (convergenceCheckResult.isPrecisionAchieved) {
break;
}
if (storm::utility::resources::isTerminate()) {
break;
}
iterationHelper->prepareNextIteration(env);
}
if (maxIter.is_initialized() && iter == maxIter.get()) {
STORM_LOG_WARN("LRA computation did not converge within " << iter << " iterations.");
} else if (storm::utility::resources::isTerminate()) {
STORM_LOG_WARN("LRA computation aborted after " << iter << " iterations.");
// We assume a Markov Automaton (with deterministic timed states and nondeterministic instant states)
storm::modelchecker::helper::internal::LraViHelper<ValueType, storm::storage::MaximalEndComponent, storm::modelchecker::helper::internal::LraViTransitionsType::DetTsNondetIs> viHelper(mec, _transitionMatrix, aperiodicFactor, _markovianStates, _exitRates);
return viHelper.performValueIteration(env, stateRewardsGetter, actionRewardsGetter, _exitRates, &this->getOptimizationDirection(), optimalChoices);
} else {
STORM_LOG_TRACE("LRA computation converged after " << iter << " iterations.");
// We assume an MDP (with nondeterministic timed states and no instant states)
storm::modelchecker::helper::internal::LraViHelper<ValueType, storm::storage::MaximalEndComponent, storm::modelchecker::helper::internal::LraViTransitionsType::NondetTsNoIs> viHelper(mec, _transitionMatrix, aperiodicFactor);
return viHelper.performValueIteration(env, stateRewardsGetter, actionRewardsGetter, nullptr, &this->getOptimizationDirection(), optimalChoices);
}
if (isProduceSchedulerSet()) {
// We will be doing one more iteration step and track scheduler choices this time.
iterationHelper->prepareNextIteration(env);
iterationHelper->iterate(env, dir, &_producedOptimalChoices.get());
}
return result;
}
template <typename ValueType>
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