SparseMarkovAutomatonCslHelper.cpp

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#include "storm/modelchecker/csl/helper/SparseMarkovAutomatonCslHelper.h"
 
#include "storm/environment/Environment.h"
#include "storm/environment/solver/EigenSolverEnvironment.h"
#include "storm/environment/solver/LongRunAverageSolverEnvironment.h"
#include "storm/environment/solver/MinMaxSolverEnvironment.h"
#include "storm/environment/solver/TimeBoundedSolverEnvironment.h"
#include "storm/environment/solver/TopologicalSolverEnvironment.h"
#include "storm/exceptions/InvalidOperationException.h"
#include "storm/exceptions/NotSupportedException.h"
#include "storm/exceptions/UncheckedRequirementException.h"
#include "storm/modelchecker/prctl/helper/SparseMdpPrctlHelper.h"
#include "storm/models/sparse/StandardRewardModel.h"
#include "storm/solver/LpSolver.h"
#include "storm/solver/MinMaxLinearEquationSolver.h"
#include "storm/solver/multiplier/Multiplier.h"
#include "storm/storage/MaximalEndComponentDecomposition.h"
#include "storm/storage/SchedulerChoice.h"
#include "storm/storage/StronglyConnectedComponentDecomposition.h"
#include "storm/storage/expressions/Expression.h"
#include "storm/storage/expressions/Variable.h"
#include "storm/utility/NumberTraits.h"
#include "storm/utility/SignalHandler.h"
#include "storm/utility/graph.h"
#include "storm/utility/macros.h"
#include "storm/utility/vector.h"
 
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:
    UnifPlusHelper(storm::storage::SparseMatrix<ValueType> const& transitionMatrix, std::vector<ValueType> const& exitRateVector,
                   storm::storage::BitVector const& markovianStates)
        : transitionMatrix(transitionMatrix), exitRateVector(exitRateVector), markovianStates(markovianStates) {
        // Intentionally left empty
    }
 
    std::vector<ValueType> computeBoundedUntilProbabilities(storm::Environment const& env, OptimizationDirection dir,
                                                            storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates,
                                                            std::optional<ValueType> const& upperTimeBound,
                                                            boost::optional<storm::storage::BitVector> const& relevantStates = boost::none) {
        // Since there is no lower time bound, we can treat the psiStates as if they are absorbing.
 
        // Compute some important subsets of states
        storm::storage::BitVector maybeStates = ~(getProb0States(dir, phiStates, psiStates) | psiStates);
        storm::storage::BitVector markovianMaybeStates = markovianStates & maybeStates;
        storm::storage::BitVector probabilisticMaybeStates = ~markovianStates & maybeStates;
        storm::storage::BitVector markovianStatesModMaybeStates = markovianMaybeStates % maybeStates;
        storm::storage::BitVector probabilisticStatesModMaybeStates = probabilisticMaybeStates % maybeStates;
        // Catch the case where this query can be solved by solving the untimed variant instead.
        // This is the case if there is no Markovian maybe state (e.g. if the initial state is already a psi state) or if there is no time bound.
        if (markovianMaybeStates.empty() || !upperTimeBound) {
            return SparseMarkovAutomatonCslHelper::computeUntilProbabilities<ValueType>(env, dir, transitionMatrix, transitionMatrix.transpose(true), phiStates,
                                                                                        psiStates, false, false)
                .values;
        }
 
        boost::optional<storm::storage::BitVector> relevantMaybeStates;
        storm::storage::BitVector relevantMarkovianMaybeStates;
        if (relevantStates) {
            if (!relevantStates->isSubsetOf(markovianStates)) {
                // Enhance the relevant states to also include Markovian states that are reachable in zero time from relevant probabilistic states
                auto const enhancedRelevantStates =
                    storm::utility::graph::getReachableStates(transitionMatrix, *relevantStates, ~markovianStates, markovianStates);
                relevantMaybeStates = enhancedRelevantStates % maybeStates;
            } else {
                relevantMaybeStates = relevantStates.get() % maybeStates;
            }
            relevantMarkovianMaybeStates = relevantMaybeStates.get() & markovianStatesModMaybeStates;
        } else {
            relevantMarkovianMaybeStates = markovianStatesModMaybeStates;
        }
        // Store the best solution known so far (useful in cases where the computation gets aborted)
        std::vector<ValueType> bestKnownSolution;
        if (relevantMaybeStates) {
            bestKnownSolution.resize(relevantMaybeStates->getNumberOfSetBits());
        }
 
        // Get the exit rates restricted to only markovian maybe states.
        std::vector<ValueType> markovianExitRates = storm::utility::vector::filterVector(exitRateVector, markovianMaybeStates);
 
        // Obtain parameters of the algorithm
        auto two = storm::utility::convertNumber<ValueType>(2.0);
        // Truncation error
        ValueType kappa = storm::utility::convertNumber<ValueType>(env.solver().timeBounded().getUnifPlusKappa());
        // Precision to be achieved
        ValueType epsilon = two * storm::utility::convertNumber<ValueType>(env.solver().timeBounded().getPrecision());
        bool relativePrecision = env.solver().timeBounded().getRelativeTerminationCriterion();
        // Uniformization rate
        ValueType lambda = *std::max_element(markovianExitRates.begin(), markovianExitRates.end());
        STORM_LOG_DEBUG("Initial lambda is " << lambda << ".");
 
        // Split the transitions into various part
        // The (uniformized) probabilities to go from a Markovian state to a psi state in one step
        std::vector<std::pair<uint64_t, ValueType>> markovianToPsiProbabilities = getSparseOneStepProbabilities(markovianMaybeStates, psiStates);
        for (auto& entry : markovianToPsiProbabilities) {
            entry.second *= markovianExitRates[entry.first] / lambda;
        }
        // Uniformized transitions from Markovian maybe states to all other maybe states. Inserts selfloop entries.
        storm::storage::SparseMatrix<ValueType> markovianToMaybeTransitions =
            getUniformizedMarkovianTransitions(markovianExitRates, lambda, maybeStates, markovianMaybeStates);
        // Transitions from probabilistic maybe states to probabilistic maybe states.
        storm::storage::SparseMatrix<ValueType> probabilisticToProbabilisticTransitions =
            transitionMatrix.getSubmatrix(true, probabilisticMaybeStates, probabilisticMaybeStates, false);
        // Transitions from probabilistic maybe states to Markovian maybe states.
        storm::storage::SparseMatrix<ValueType> probabilisticToMarkovianTransitions =
            transitionMatrix.getSubmatrix(true, probabilisticMaybeStates, markovianMaybeStates, false);
        // The probabilities to go from a probabilistic state to a psi state in one step
        std::vector<std::pair<uint64_t, ValueType>> probabilisticToPsiProbabilities = getSparseOneStepProbabilities(probabilisticMaybeStates, psiStates);
 
        // Set up a solver for the transitions between probabilistic states (if there are some)
        Environment solverEnv = env;
        solverEnv.solver().setForceExact(true);  // Errors within the inner iterations can propagate significantly
        auto solver = setUpProbabilisticStatesSolver(solverEnv, dir, probabilisticToProbabilisticTransitions);
 
        // Allocate auxiliary memory that can be used during the iterations
        std::vector<ValueType> maybeStatesValuesLower(maybeStates.getNumberOfSetBits(), storm::utility::zero<ValueType>());          // should be zero initially
        std::vector<ValueType> maybeStatesValuesWeightedUpper(maybeStates.getNumberOfSetBits(), storm::utility::zero<ValueType>());  // should be zero initially
        std::vector<ValueType> maybeStatesValuesUpper(maybeStates.getNumberOfSetBits(), storm::utility::zero<ValueType>());          // should be zero initially
        std::vector<ValueType> nextMarkovianStateValues = std::move(
            markovianExitRates);  // At this point, the markovianExitRates are no longer needed, so we 'move' them away instead of allocating new memory
        std::vector<ValueType> nextProbabilisticStateValues(probabilisticToProbabilisticTransitions.getRowGroupCount());
        std::vector<ValueType> eqSysRhs(probabilisticToProbabilisticTransitions.getRowCount());
 
        // Start the outer iterations which increase the uniformization rate until lower and upper bound on the result vector is sufficiently small
        storm::utility::ProgressMeasurement progressIterations("iterations");
        uint64_t iteration = 0;
        progressIterations.startNewMeasurement(iteration);
        bool converged = false;
        bool abortedInnerIterations = false;
        while (!converged) {
            // Maximal step size
            uint64_t N = storm::utility::ceil(lambda * *upperTimeBound * std::exp(2) - storm::utility::log(kappa * epsilon));
            // Compute poisson distribution.
            // The division by 8 is similar to what is done for CTMCs (probably to reduce numerical impacts?)
            auto foxGlynnResult =
                storm::utility::numerical::foxGlynn(lambda * *upperTimeBound, epsilon * kappa / storm::utility::convertNumber<ValueType>(8.0));
            // Scale the weights so they sum to one.
            // storm::utility::vector::scaleVectorInPlace(foxGlynnResult.weights, storm::utility::one<ValueType>() / foxGlynnResult.totalWeight);
 
            // Set up multiplier
            auto markovianToMaybeMultiplier = storm::solver::MultiplierFactory<ValueType>().create(env, markovianToMaybeTransitions);
            auto probabilisticToMarkovianMultiplier = storm::solver::MultiplierFactory<ValueType>().create(env, probabilisticToMarkovianTransitions);
 
            // Perform inner iterations first for upper, then for lower bound
            STORM_LOG_ASSERT(!storm::utility::vector::hasNonZeroEntry(maybeStatesValuesUpper), "Current values need to be initialized with zero.");
            for (bool computeLowerBound : {false, true}) {
                auto& maybeStatesValues = computeLowerBound ? maybeStatesValuesLower : maybeStatesValuesWeightedUpper;
                ValueType targetValue = computeLowerBound ? storm::utility::zero<ValueType>() : storm::utility::one<ValueType>();
                storm::utility::ProgressMeasurement progressSteps("steps in iteration " + std::to_string(iteration) + " for " +
                                                                  std::string(computeLowerBound ? "lower" : "upper") + " bounds.");
                progressSteps.setMaxCount(N);
                progressSteps.startNewMeasurement(0);
                bool firstIteration = true;  // The first iterations can be irrelevant, because they will only produce zeroes anyway.
                int64_t k = N;
                // Iteration k = N is always non-relevant
                for (--k; k >= 0; --k) {
                    // Check whether the iteration is relevant, that is, whether it will contribute non-zero values to the overall result
                    if (computeLowerBound) {
                        // Check whether the value for visiting a target state will be zero.
                        if (static_cast<uint64_t>(k) > foxGlynnResult.right) {
                            // Reaching this point means that we are in one of the earlier iterations where fox glynn told us to cut off
                            continue;
                        }
                    } else {
                        uint64_t i = N - 1 - k;
                        if (i > foxGlynnResult.right) {
                            // Reaching this point means that we are in a later iteration which will not contribute to the upper bound
                            // Since i will only get larger in subsequent iterations, we can directly break here.
                            break;
                        }
                    }
 
                    // Compute the values at Markovian maybe states.
                    if (firstIteration) {
                        firstIteration = false;
                        // Reaching this point means that this is the very first relevant iteration.
                        // If we are in the very first relevant iteration, we know that all states from the previous iteration have value zero.
                        // It is therefore valid (and necessary) to just set the values of Markovian states to zero.
                        std::fill(nextMarkovianStateValues.begin(), nextMarkovianStateValues.end(), storm::utility::zero<ValueType>());
                    } else {
                        // Compute the values at Markovian maybe states.
                        markovianToMaybeMultiplier->multiply(env, maybeStatesValues, nullptr, nextMarkovianStateValues);
                        for (auto const& oneStepProb : markovianToPsiProbabilities) {
                            nextMarkovianStateValues[oneStepProb.first] += oneStepProb.second * targetValue;
                        }
                    }
 
                    // Update the value when reaching a psi state.
                    // This has to be done after updating the Markovian state values since we needed the 'old' target value above.
                    if (computeLowerBound && static_cast<uint64_t>(k) >= foxGlynnResult.left) {
                        STORM_LOG_ASSERT(static_cast<uint64_t>(k) <= foxGlynnResult.right,
                                         "K exceeds left bound.");  // has to hold since this iteration is relevant
                        targetValue += foxGlynnResult.weights[k - foxGlynnResult.left];
                    }
 
                    // Compute the values at probabilistic states.
                    probabilisticToMarkovianMultiplier->multiply(env, nextMarkovianStateValues, nullptr, eqSysRhs);
                    for (auto const& oneStepProb : probabilisticToPsiProbabilities) {
                        eqSysRhs[oneStepProb.first] += oneStepProb.second * targetValue;
                    }
                    if (solver) {
                        solver->solveEquations(solverEnv, dir, nextProbabilisticStateValues, eqSysRhs);
                    } else {
                        storm::utility::vector::reduceVectorMinOrMax(dir, eqSysRhs, nextProbabilisticStateValues,
                                                                     probabilisticToProbabilisticTransitions.getRowGroupIndices());
                    }
 
                    // Create the new values for the maybestates
                    // Fuse the results together
                    storm::utility::vector::setVectorValues(maybeStatesValues, markovianStatesModMaybeStates, nextMarkovianStateValues);
                    storm::utility::vector::setVectorValues(maybeStatesValues, probabilisticStatesModMaybeStates, nextProbabilisticStateValues);
                    if (!computeLowerBound) {
                        // Add the scaled values to the actual result vector
                        uint64_t i = N - 1 - k;
                        if (i >= foxGlynnResult.left) {
                            STORM_LOG_ASSERT(i <= foxGlynnResult.right, "I exceeds right bound.");  // has to hold since this iteration is considered relevant.
                            ValueType const& weight = foxGlynnResult.weights[i - foxGlynnResult.left];
                            storm::utility::vector::addScaledVector(maybeStatesValuesUpper, maybeStatesValuesWeightedUpper, weight);
                        }
                    }
 
                    progressSteps.updateProgress(N - k);
                    if (storm::utility::resources::isTerminate()) {
                        abortedInnerIterations = true;
                        break;
                    }
                }
 
                if (computeLowerBound) {
                    storm::utility::vector::scaleVectorInPlace(maybeStatesValuesLower, storm::utility::one<ValueType>() / foxGlynnResult.totalWeight);
                } else {
                    storm::utility::vector::scaleVectorInPlace(maybeStatesValuesUpper, storm::utility::one<ValueType>() / foxGlynnResult.totalWeight);
                }
 
                if (abortedInnerIterations || storm::utility::resources::isTerminate()) {
                    break;
                }
 
                // Check if the lower and upper bound are sufficiently close to each other
                converged = checkConvergence(maybeStatesValuesLower, maybeStatesValuesUpper, relevantMarkovianMaybeStates, epsilon, relativePrecision, kappa);
                if (converged) {
                    break;
                }
 
                // Store the best solution we have found so far.
                if (relevantMaybeStates) {
                    auto currentSolIt = bestKnownSolution.begin();
                    for (uint64_t state : relevantMaybeStates.get()) {
                        // We take the average of the lower and upper bounds
                        *currentSolIt = (maybeStatesValuesLower[state] + maybeStatesValuesUpper[state]) / two;
                        ++currentSolIt;
                    }
                }
            }
 
            if (!converged) {
                // Increase the uniformization rate and prepare the next run
 
                // Double lambda.
                ValueType oldLambda = lambda;
                lambda *= two;
                STORM_LOG_DEBUG("Increased lambda to " << lambda << ".");
 
                if (relativePrecision) {
                    // Reduce kappa a bit
                    ValueType minValue;
                    if (relevantMaybeStates) {
                        minValue = storm::utility::vector::min_if(maybeStatesValuesUpper, relevantMaybeStates.get());
                    } else {
                        minValue = *std::min_element(maybeStatesValuesUpper.begin(), maybeStatesValuesUpper.end());
                    }
                    minValue *= storm::utility::convertNumber<ValueType>(env.solver().timeBounded().getUnifPlusKappa());
                    kappa = std::min(kappa, minValue);
                    STORM_LOG_DEBUG("Decreased kappa to " << kappa << ".");
                }
 
                // Apply uniformization with new rate
                uniformize(markovianToMaybeTransitions, markovianToPsiProbabilities, oldLambda, lambda, markovianStatesModMaybeStates);
 
                // Reset the values of the maybe states to zero.
                std::fill(maybeStatesValuesUpper.begin(), maybeStatesValuesUpper.end(), storm::utility::zero<ValueType>());
            }
            progressIterations.updateProgress(++iteration);
            if (storm::utility::resources::isTerminate()) {
                STORM_LOG_WARN("Aborted unif+ in iteration " << iteration << ".");
                break;
            }
        }
 
        // Prepare the result vector
        std::vector<ValueType> result(transitionMatrix.getRowGroupCount(), storm::utility::zero<ValueType>());
        // Set values of target states to 1
        storm::utility::vector::setVectorValues(result, psiStates, storm::utility::one<ValueType>());
 
        if (abortedInnerIterations && iteration > 1 && relevantMaybeStates && relevantStates) {
            // We should take the stored solution instead of the current (probably more incorrect) lower/upper values
            storm::utility::vector::setVectorValues(result, relevantMaybeStates.get(), bestKnownSolution);
        } else {
            // Set the values for Markovian "maybe" states
            STORM_LOG_ASSERT(markovianMaybeStates.getNumberOfSetBits() == markovianStatesModMaybeStates.getNumberOfSetBits(),
                             "Unexpected number of Markovian maybe states.");
            auto subStateIt = markovianStatesModMaybeStates.begin();
            for (uint64_t markovianState : markovianMaybeStates) {
                result[markovianState] = (maybeStatesValuesLower[*subStateIt] + maybeStatesValuesUpper[*subStateIt]) / two;
                ++subStateIt;
            }
            // At this point, we only need to set the values for probabilistic "maybe" states.
            // We derive the values from the values of the other states, which are reached in zero time.
            uint64_t probSubState = 0;
            for (uint64_t probabilisticState : probabilisticMaybeStates) {
                for (auto const probStateRow : transitionMatrix.getRowGroupIndices(probabilisticState)) {
                    eqSysRhs[probSubState] = transitionMatrix.multiplyRowWithVector(probStateRow, result);
                    ++probSubState;
                }
            }
            if (solver) {
                solver->solveEquations(solverEnv, dir, nextProbabilisticStateValues, eqSysRhs);
            } else {
                storm::utility::vector::reduceVectorMinOrMax(dir, eqSysRhs, nextProbabilisticStateValues,
                                                             probabilisticToProbabilisticTransitions.getRowGroupIndices());
            }
            storm::utility::vector::setVectorValues(result, probabilisticMaybeStates, nextProbabilisticStateValues);
        }
        return result;
    }
 
   private:
    bool checkConvergence(std::vector<ValueType> const& lower, std::vector<ValueType> const& upper, storm::storage::BitVector const& relevantValues,
                          ValueType const& epsilon, bool relative, ValueType& kappa) {
        STORM_LOG_ASSERT(relevantValues.size() == lower.size(), "Relevant values size mismatch.");
        if (!relative) {
            return storm::utility::vector::equalModuloPrecision(lower, upper, relevantValues, epsilon * (storm::utility::one<ValueType>() - kappa), false);
        }
        ValueType truncationError = epsilon * kappa;
        for (uint64_t const i : relevantValues) {
            if (lower[i] == upper[i]) {
                continue;
            }
            if (lower[i] <= truncationError) {
                return false;
            }
            ValueType absDiff = upper[i] - lower[i] + truncationError;
            ValueType relDiff = absDiff / lower[i];
            if (relDiff > epsilon) {
                return false;
            }
            STORM_LOG_ASSERT(absDiff > storm::utility::zero<ValueType>(), "Upper bound " << upper[i] << " is smaller than lower bound " << lower[i] << ".");
        }
        return true;
    }
 
    storm::storage::SparseMatrix<ValueType> getUniformizedMarkovianTransitions(std::vector<ValueType> const& oldRates, ValueType uniformizationRate,
                                                                               storm::storage::BitVector const& maybeStates,
                                                                               storm::storage::BitVector const& markovianMaybeStates) {
        // We need a submatrix whose rows correspond to the markovian states and columns correpsond to the maybestates.
        // In addition, we need 'selfloop' entries for the markovian maybe states.
 
        // First build a submatrix without selfloop entries
        auto submatrix = transitionMatrix.getSubmatrix(true, markovianMaybeStates, maybeStates);
        STORM_LOG_ASSERT(submatrix.getRowCount() == submatrix.getRowGroupCount(), "Submatrix row count != row group count.");
 
        // Now add selfloop entries at the correct positions and apply uniformization
        storm::storage::SparseMatrixBuilder<ValueType> builder(submatrix.getRowCount(), submatrix.getColumnCount());
        auto markovianStateColumns = markovianMaybeStates % maybeStates;
        uint64_t row = 0;
        for (uint64_t selfloopColumn : markovianStateColumns) {
            ValueType const& oldExitRate = oldRates[row];
            bool foundSelfoop = false;
            for (auto const& entry : submatrix.getRow(row)) {
                if (entry.getColumn() == selfloopColumn) {
                    foundSelfoop = true;
                    ValueType newSelfLoop = uniformizationRate - oldExitRate + entry.getValue() * oldExitRate;
                    builder.addNextValue(row, entry.getColumn(), newSelfLoop / uniformizationRate);
                } else {
                    builder.addNextValue(row, entry.getColumn(), entry.getValue() * oldExitRate / uniformizationRate);
                }
            }
            if (!foundSelfoop) {
                ValueType newSelfLoop = uniformizationRate - oldExitRate;
                builder.addNextValue(row, selfloopColumn, newSelfLoop / uniformizationRate);
            }
            ++row;
        }
        STORM_LOG_ASSERT(row == submatrix.getRowCount(), "Row count mismatch for submatrix.");
 
        return builder.build();
    }
 
    void uniformize(storm::storage::SparseMatrix<ValueType>& matrix, std::vector<std::pair<uint64_t, ValueType>>& oneSteps,
                    std::vector<ValueType> const& oldRates, ValueType uniformizationRate, storm::storage::BitVector const& selfloopColumns) {
        uint64_t row = 0;
        for (uint64_t selfloopColumn : selfloopColumns) {
            ValueType const& oldExitRate = oldRates[row];
            if (oldExitRate == uniformizationRate) {
                // Already uniformized.
                ++row;
                continue;
            }
            for (auto& v : matrix.getRow(row)) {
                if (v.getColumn() == selfloopColumn) {
                    ValueType newSelfLoop = uniformizationRate - oldExitRate + v.getValue() * oldExitRate;
                    v.setValue(newSelfLoop / uniformizationRate);
                } else {
                    v.setValue(v.getValue() * oldExitRate / uniformizationRate);
                }
            }
            ++row;
        }
        STORM_LOG_ASSERT(row == matrix.getRowCount(), "Row count mismatch after uniformization.");
        for (auto& oneStep : oneSteps) {
            oneStep.second *= oldRates[oneStep.first] / uniformizationRate;
        }
    }

    void uniformize(storm::storage::SparseMatrix<ValueType>& matrix, std::vector<std::pair<uint64_t, ValueType>>& oneSteps, ValueType oldUniformizationRate,
                    ValueType newUniformizationRate, storm::storage::BitVector const& selfloopColumns) {
        if (oldUniformizationRate != newUniformizationRate) {
            STORM_LOG_ASSERT(oldUniformizationRate < newUniformizationRate, "Old uniformization rate must be less than new.");
            ValueType rateDiff = newUniformizationRate - oldUniformizationRate;
            ValueType rateFraction = oldUniformizationRate / newUniformizationRate;
            uint64_t row = 0;
            for (uint64_t selfloopColumn : selfloopColumns) {
                for (auto& v : matrix.getRow(row)) {
                    if (v.getColumn() == selfloopColumn) {
                        ValueType newSelfLoop = rateDiff + v.getValue() * oldUniformizationRate;
                        v.setValue(newSelfLoop / newUniformizationRate);
                    } else {
                        v.setValue(v.getValue() * rateFraction);
                    }
                }
                ++row;
            }
            STORM_LOG_ASSERT(row == matrix.getRowCount(), "Row count mismatch after uniformization.");
            for (auto& oneStep : oneSteps) {
                oneStep.second *= rateFraction;
            }
        }
    }
 
    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);
        } else {
            return storm::utility::graph::performProb0E(transitionMatrix, transitionMatrix.getRowGroupIndices(), transitionMatrix.transpose(true), phiStates,
                                                        psiStates);
        }
    }

    std::vector<std::pair<uint64_t, ValueType>> getSparseOneStepProbabilities(storm::storage::BitVector const& sourceStateConstraint,
                                                                              storm::storage::BitVector const& targetStateConstraint) const {
        auto denseResult = transitionMatrix.getConstrainedRowGroupSumVector(sourceStateConstraint, targetStateConstraint);
        std::vector<std::pair<uint64_t, ValueType>> sparseResult;
        for (uint64_t i = 0; i < denseResult.size(); ++i) {
            auto const& val = denseResult[i];
            if (!storm::utility::isZero(val)) {
                sparseResult.emplace_back(i, val);
            }
        }
        return sparseResult;
    }
 
    storm::storage::SparseMatrix<ValueType> const& transitionMatrix;
    std::vector<ValueType> const& exitRateVector;
    storm::storage::BitVector const& markovianStates;
};
 
template<typename ValueType>
void computeBoundedReachabilityProbabilitiesImca(Environment const& env, OptimizationDirection dir,
                                                 storm::storage::SparseMatrix<ValueType> const& transitionMatrix, std::vector<ValueType> const& exitRates,
                                                 storm::storage::BitVector const& goalStates, storm::storage::BitVector const& markovianNonGoalStates,
                                                 storm::storage::BitVector const& probabilisticNonGoalStates, std::vector<ValueType>& markovianNonGoalValues,
                                                 std::vector<ValueType>& probabilisticNonGoalValues, ValueType delta, uint64_t numberOfSteps) {
    // Start by computing four sparse matrices:
    // * a matrix aMarkovian with all (discretized) transitions from Markovian non-goal states to all Markovian non-goal states.
    // * a matrix aMarkovianToProbabilistic with all (discretized) transitions from Markovian non-goal states to all probabilistic non-goal states.
    // * a matrix aProbabilistic with all (non-discretized) transitions from probabilistic non-goal states to other probabilistic non-goal states.
    // * a matrix aProbabilisticToMarkovian with all (non-discretized) transitions from probabilistic non-goal states to all Markovian non-goal states.
    typename storm::storage::SparseMatrix<ValueType> aMarkovian = transitionMatrix.getSubmatrix(true, markovianNonGoalStates, markovianNonGoalStates, true);
 
    bool existProbabilisticStates = !probabilisticNonGoalStates.empty();
    typename storm::storage::SparseMatrix<ValueType> aMarkovianToProbabilistic;
    typename storm::storage::SparseMatrix<ValueType> aProbabilistic;
    typename storm::storage::SparseMatrix<ValueType> aProbabilisticToMarkovian;
    if (existProbabilisticStates) {
        aMarkovianToProbabilistic = transitionMatrix.getSubmatrix(true, markovianNonGoalStates, probabilisticNonGoalStates);
        aProbabilistic = transitionMatrix.getSubmatrix(true, probabilisticNonGoalStates, probabilisticNonGoalStates);
        aProbabilisticToMarkovian = transitionMatrix.getSubmatrix(true, probabilisticNonGoalStates, markovianNonGoalStates);
    }
 
    // The matrices with transitions from Markovian states need to be digitized.
    // Digitize aMarkovian. Based on whether the transition is a self-loop or not, we apply the two digitization rules.
    uint64_t rowIndex = 0;
    for (uint64_t state : markovianNonGoalStates) {
        for (auto& element : aMarkovian.getRow(rowIndex)) {
            ValueType eTerm = std::exp(-exitRates[state] * delta);
            if (element.getColumn() == rowIndex) {
                element.setValue((storm::utility::one<ValueType>() - eTerm) * element.getValue() + eTerm);
            } else {
                element.setValue((storm::utility::one<ValueType>() - eTerm) * element.getValue());
            }
        }
        ++rowIndex;
    }
 
    // Digitize aMarkovianToProbabilistic. As there are no self-loops in this case, we only need to apply the digitization formula for regular successors.
    if (existProbabilisticStates) {
        rowIndex = 0;
        for (uint64_t state : markovianNonGoalStates) {
            for (auto& element : aMarkovianToProbabilistic.getRow(rowIndex)) {
                element.setValue((1 - std::exp(-exitRates[state] * delta)) * element.getValue());
            }
            ++rowIndex;
        }
    }
 
    // Initialize the two vectors that hold the variable one-step probabilities to all target states for probabilistic and Markovian (non-goal) states.
    std::vector<ValueType> bProbabilistic(existProbabilisticStates ? aProbabilistic.getRowCount() : 0);
    std::vector<ValueType> bMarkovian(markovianNonGoalStates.getNumberOfSetBits());
 
    // Compute the two fixed right-hand side vectors, one for Markovian states and one for the probabilistic ones.
    std::vector<ValueType> bProbabilisticFixed;
    if (existProbabilisticStates) {
        bProbabilisticFixed = transitionMatrix.getConstrainedRowGroupSumVector(probabilisticNonGoalStates, goalStates);
    }
    std::vector<ValueType> bMarkovianFixed;
    bMarkovianFixed.reserve(markovianNonGoalStates.getNumberOfSetBits());
    for (uint64_t state : markovianNonGoalStates) {
        bMarkovianFixed.push_back(storm::utility::zero<ValueType>());
 
        for (auto& element : transitionMatrix.getRowGroup(state)) {
            if (goalStates.get(element.getColumn())) {
                bMarkovianFixed.back() += (1 - std::exp(-exitRates[state] * delta)) * element.getValue();
            }
        }
    }
 
    // 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:
    // *    perform value iteration using A_PSwG, v_PS and the vector b where b = (A * 1_G)|PS + A_PStoMS * v_MS
    //      and 1_G being the characteristic vector for all goal states.
    // *    perform one timed-step using v_MS := A_MSwG * v_MS + A_MStoPS * v_PS + (A * 1_G)|MS
    std::vector<ValueType> markovianNonGoalValuesSwap(markovianNonGoalValues);
    for (uint64_t currentStep = 0; currentStep < numberOfSteps; ++currentStep) {
        if (existProbabilisticStates) {
            // Start by (re-)computing bProbabilistic = bProbabilisticFixed + aProbabilisticToMarkovian * vMarkovian.
            aProbabilisticToMarkovian.multiplyWithVector(markovianNonGoalValues, bProbabilistic);
            storm::utility::vector::addVectors(bProbabilistic, bProbabilisticFixed, bProbabilistic);
 
            // Now perform the inner value iteration for probabilistic states.
            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);
            storm::utility::vector::addVectors(bMarkovian, bMarkovianFixed, bMarkovian);
        }
 
        aMarkovian.multiplyWithVector(markovianNonGoalValues, markovianNonGoalValuesSwap);
        std::swap(markovianNonGoalValues, markovianNonGoalValuesSwap);
        if (existProbabilisticStates) {
            storm::utility::vector::addVectors(markovianNonGoalValues, bMarkovian, markovianNonGoalValues);
        } else {
            storm::utility::vector::addVectors(markovianNonGoalValues, bMarkovianFixed, markovianNonGoalValues);
        }
        if (storm::utility::resources::isTerminate()) {
            break;
        }
    }
 
    if (existProbabilisticStates) {
        // 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);
        if (solver) {
            solver->solveEquations(solverEnv, dir, probabilisticNonGoalValues, bProbabilistic);
        } else {
            storm::utility::vector::reduceVectorMinOrMax(dir, bProbabilistic, probabilisticNonGoalValues, aProbabilistic.getRowGroupIndices());
        }
    }
}
 
template<typename ValueType>
std::vector<ValueType> computeBoundedUntilProbabilitiesImca(Environment const& env, OptimizationDirection dir,
                                                            storm::storage::SparseMatrix<ValueType> const& transitionMatrix,
                                                            std::vector<ValueType> const& exitRateVector, storm::storage::BitVector const& markovianStates,
                                                            storm::storage::BitVector const& psiStates,
                                                            std::pair<double, std::optional<double>> const& boundsPair) {
    STORM_LOG_TRACE("Using IMCA's technique to compute bounded until probabilities.");
 
    uint64_t numberOfStates = transitionMatrix.getRowGroupCount();
 
    // 'Unpack' the bounds to make them more easily accessible.
    STORM_LOG_THROW(boundsPair.second.has_value(), storm::exceptions::NotSupportedException,
                    "IMCA's technique requires an upper time bound, but the given property has none.");
    double lowerBound = boundsPair.first;
    double upperBound = *boundsPair.second;
 
    // (1) Compute the accuracy we need to achieve the required error bound.
    ValueType maxExitRate = 0;
    for (auto value : exitRateVector) {
        maxExitRate = std::max(maxExitRate, value);
    }
    ValueType delta = (2.0 * storm::utility::convertNumber<ValueType>(env.solver().timeBounded().getPrecision())) / (upperBound * maxExitRate * maxExitRate);
 
    // (2) Compute the number of steps we need to make for the interval.
    uint64_t numberOfSteps = static_cast<uint64_t>(std::ceil((upperBound - lowerBound) / delta));
    STORM_LOG_INFO("Performing " << numberOfSteps << " iterations (delta=" << delta << ") for interval [" << lowerBound << ", " << upperBound << "].\n");
 
    // (3) Compute the non-goal states and initialize two vectors
    // * vProbabilistic holds the probability values of probabilistic non-goal states.
    // * vMarkovian holds the probability values of Markovian non-goal states.
    storm::storage::BitVector const& markovianNonGoalStates = markovianStates & ~psiStates;
    storm::storage::BitVector const& probabilisticNonGoalStates = ~markovianStates & ~psiStates;
    std::vector<ValueType> vProbabilistic(probabilisticNonGoalStates.getNumberOfSetBits());
    std::vector<ValueType> vMarkovian(markovianNonGoalStates.getNumberOfSetBits());
 
    computeBoundedReachabilityProbabilitiesImca(env, dir, transitionMatrix, exitRateVector, psiStates, markovianNonGoalStates, probabilisticNonGoalStates,
                                                vMarkovian, vProbabilistic, delta, numberOfSteps);
 
    // (4) If the lower bound of interval was non-zero, we need to take the current values as the starting values for a subsequent value iteration.
    if (lowerBound != storm::utility::zero<ValueType>()) {
        std::vector<ValueType> vAllProbabilistic((~markovianStates).getNumberOfSetBits());
        std::vector<ValueType> vAllMarkovian(markovianStates.getNumberOfSetBits());
 
        // Create the starting value vectors for the next value iteration based on the results of the previous one.
        storm::utility::vector::setVectorValues<ValueType>(vAllProbabilistic, psiStates % ~markovianStates, storm::utility::one<ValueType>());
        storm::utility::vector::setVectorValues<ValueType>(vAllProbabilistic, ~psiStates % ~markovianStates, vProbabilistic);
        storm::utility::vector::setVectorValues<ValueType>(vAllMarkovian, psiStates % markovianStates, storm::utility::one<ValueType>());
        storm::utility::vector::setVectorValues<ValueType>(vAllMarkovian, ~psiStates % markovianStates, vMarkovian);
 
        // Compute the number of steps to reach the target interval.
        numberOfSteps = static_cast<uint64_t>(std::ceil(lowerBound / delta));
        STORM_LOG_INFO("Performing " << numberOfSteps << " iterations (delta=" << delta << ") for interval [0, " << lowerBound << "].\n");
 
        // Compute the bounded reachability for interval [0, b-a].
        computeBoundedReachabilityProbabilitiesImca(env, dir, transitionMatrix, exitRateVector, storm::storage::BitVector(numberOfStates), markovianStates,
                                                    ~markovianStates, vAllMarkovian, vAllProbabilistic, delta, numberOfSteps);
 
        // Create the result vector out of vAllProbabilistic and vAllMarkovian and return it.
        std::vector<ValueType> result(numberOfStates, storm::utility::zero<ValueType>());
        storm::utility::vector::setVectorValues(result, ~markovianStates, vAllProbabilistic);
        storm::utility::vector::setVectorValues(result, markovianStates, vAllMarkovian);
 
        return result;
    } else {
        // Create the result vector out of 1_G, vProbabilistic and vMarkovian and return it.
        std::vector<ValueType> result(numberOfStates);
        storm::utility::vector::setVectorValues<ValueType>(result, psiStates, storm::utility::one<ValueType>());
        storm::utility::vector::setVectorValues(result, probabilisticNonGoalStates, vProbabilistic);
        storm::utility::vector::setVectorValues(result, markovianNonGoalStates, vMarkovian);
        return result;
    }
}
 
template<typename ValueType, typename std::enable_if<storm::NumberTraits<ValueType>::SupportsExponential, int>::type>
std::vector<ValueType> SparseMarkovAutomatonCslHelper::computeBoundedUntilProbabilities(
    Environment const& env, storm::solver::SolveGoal<ValueType>&& goal, storm::storage::SparseMatrix<ValueType> const& transitionMatrix,
    std::vector<ValueType> const& exitRateVector, storm::storage::BitVector const& markovianStates, storm::storage::BitVector const& phiStates,
    storm::storage::BitVector const& psiStates, std::pair<double, std::optional<double>> const& boundsPair) {
    STORM_LOG_THROW(!env.solver().isForceExact(), storm::exceptions::InvalidOperationException,
                    "Exact computations not possible for bounded until probabilities.");
 
    // Choose the applicable method
    auto method = env.solver().timeBounded().getMaMethod();
    if (method == storm::solver::MaBoundedReachabilityMethod::Imca) {
        if (!phiStates.full()) {
            STORM_LOG_WARN("Using Unif+ method because IMCA method does not support (phi Until psi) for non-trivial phi");
            method = storm::solver::MaBoundedReachabilityMethod::UnifPlus;
        }
    } else {
        STORM_LOG_ASSERT(method == storm::solver::MaBoundedReachabilityMethod::UnifPlus, "Unknown solution method.");
        if (!storm::utility::isZero(boundsPair.first)) {
            STORM_LOG_WARN("Using IMCA method because Unif+ does not support a lower bound > 0.");
            method = storm::solver::MaBoundedReachabilityMethod::Imca;
        }
    }
 
    if (method == storm::solver::MaBoundedReachabilityMethod::Imca) {
        return computeBoundedUntilProbabilitiesImca(env, goal.direction(), transitionMatrix, exitRateVector, markovianStates, psiStates, boundsPair);
    } else {
        UnifPlusHelper<ValueType> helper(transitionMatrix, exitRateVector, markovianStates);
        boost::optional<storm::storage::BitVector> relevantValues;
        if (goal.hasRelevantValues()) {
            relevantValues = std::move(goal.relevantValues());
        }
        return helper.computeBoundedUntilProbabilities(env, goal.direction(), phiStates, psiStates, boundsPair.second, relevantValues);
    }
}
 
template<typename ValueType, typename std::enable_if<!storm::NumberTraits<ValueType>::SupportsExponential, int>::type>
std::vector<ValueType> SparseMarkovAutomatonCslHelper::computeBoundedUntilProbabilities(Environment const&, storm::solver::SolveGoal<ValueType>&&,
                                                                                        storm::storage::SparseMatrix<ValueType> const&,
                                                                                        std::vector<ValueType> const&, storm::storage::BitVector const&,
                                                                                        storm::storage::BitVector const&, storm::storage::BitVector const&,
                                                                                        std::pair<double, std::optional<double>> const&) {
    STORM_LOG_THROW(false, storm::exceptions::InvalidOperationException, "Computing bounded until probabilities is unsupported for this value type.");
}
 
template<typename ValueType>
MDPSparseModelCheckingHelperReturnType<ValueType> SparseMarkovAutomatonCslHelper::computeUntilProbabilities(
    Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix,
    storm::storage::SparseMatrix<ValueType> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates,
    bool qualitative, bool produceScheduler) {
    return storm::modelchecker::helper::SparseMdpPrctlHelper<ValueType>::computeUntilProbabilities(env, dir, transitionMatrix, backwardTransitions, phiStates,
                                                                                                   psiStates, qualitative, produceScheduler);
}
 
template<typename ValueType, typename RewardModelType>
MDPSparseModelCheckingHelperReturnType<ValueType> SparseMarkovAutomatonCslHelper::computeTotalRewards(
    Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix,
    storm::storage::SparseMatrix<ValueType> const& backwardTransitions, std::vector<ValueType> const& exitRateVector,
    storm::storage::BitVector const& markovianStates, RewardModelType const& rewardModel, bool produceScheduler) {
    // Get a reward model where the state rewards are scaled accordingly
    std::vector<ValueType> stateRewardWeights(transitionMatrix.getRowGroupCount(), storm::utility::zero<ValueType>());
    for (uint64_t markovianState : markovianStates) {
        stateRewardWeights[markovianState] = storm::utility::one<ValueType>() / exitRateVector[markovianState];
    }
    std::vector<ValueType> totalRewardVector = rewardModel.getTotalActionRewardVector(transitionMatrix, stateRewardWeights);
    RewardModelType scaledRewardModel(std::nullopt, std::move(totalRewardVector));
 
    return SparseMdpPrctlHelper<ValueType>::computeTotalRewards(env, dir, transitionMatrix, backwardTransitions, scaledRewardModel, false, produceScheduler);
}
 
template<typename ValueType, typename RewardModelType>
MDPSparseModelCheckingHelperReturnType<ValueType> SparseMarkovAutomatonCslHelper::computeReachabilityRewards(
    Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix,
    storm::storage::SparseMatrix<ValueType> const& backwardTransitions, std::vector<ValueType> const& exitRateVector,
    storm::storage::BitVector const& markovianStates, RewardModelType const& rewardModel, storm::storage::BitVector const& psiStates, bool produceScheduler) {
    // Get a reward model where the state rewards are scaled accordingly
    std::vector<ValueType> stateRewardWeights(transitionMatrix.getRowGroupCount(), storm::utility::zero<ValueType>());
    for (uint64_t markovianState : markovianStates) {
        stateRewardWeights[markovianState] = storm::utility::one<ValueType>() / exitRateVector[markovianState];
    }
    std::vector<ValueType> totalRewardVector = rewardModel.getTotalActionRewardVector(transitionMatrix, stateRewardWeights);
    RewardModelType scaledRewardModel(std::nullopt, std::move(totalRewardVector));
 
    return SparseMdpPrctlHelper<ValueType>::computeReachabilityRewards(env, dir, transitionMatrix, backwardTransitions, scaledRewardModel, psiStates, false,
                                                                       produceScheduler);
}
 
template<typename ValueType>
MDPSparseModelCheckingHelperReturnType<ValueType> SparseMarkovAutomatonCslHelper::computeReachabilityTimes(
    Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<ValueType> const& transitionMatrix,
    storm::storage::SparseMatrix<ValueType> const& backwardTransitions, std::vector<ValueType> const& exitRateVector,
    storm::storage::BitVector const& markovianStates, storm::storage::BitVector const& psiStates, bool produceScheduler) {
    // Get a reward model representing expected sojourn times
    std::vector<ValueType> rewardValues(transitionMatrix.getRowCount(), storm::utility::zero<ValueType>());
    for (uint64_t markovianState : markovianStates) {
        rewardValues[transitionMatrix.getRowGroupIndices()[markovianState]] = storm::utility::one<ValueType>() / exitRateVector[markovianState];
    }
    storm::models::sparse::StandardRewardModel<ValueType> rewardModel(std::nullopt, std::move(rewardValues));
 
    return SparseMdpPrctlHelper<ValueType>::computeReachabilityRewards(env, dir, transitionMatrix, backwardTransitions, rewardModel, psiStates, false,
                                                                       produceScheduler);
}
 
template std::vector<double> SparseMarkovAutomatonCslHelper::computeBoundedUntilProbabilities(
    Environment const& env, storm::solver::SolveGoal<double>&& goal, storm::storage::SparseMatrix<double> const& transitionMatrix,
    std::vector<double> const& exitRateVector, storm::storage::BitVector const& markovianStates, storm::storage::BitVector const& phiStates,
    storm::storage::BitVector const& psiStates, std::pair<double, std::optional<double>> const& boundsPair);
 
template MDPSparseModelCheckingHelperReturnType<double> SparseMarkovAutomatonCslHelper::computeUntilProbabilities(
    Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix,
    storm::storage::SparseMatrix<double> const& backwardTransitions, storm::storage::BitVector const& phiStates, storm::storage::BitVector const& psiStates,
    bool qualitative, bool produceScheduler);
 
template MDPSparseModelCheckingHelperReturnType<double> SparseMarkovAutomatonCslHelper::computeReachabilityRewards(
    Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix,
    storm::storage::SparseMatrix<double> const& backwardTransitions, std::vector<double> const& exitRateVector,
    storm::storage::BitVector const& markovianStates, storm::models::sparse::StandardRewardModel<double> const& rewardModel,
    storm::storage::BitVector const& psiStates, bool produceScheduler);
 
template MDPSparseModelCheckingHelperReturnType<double> SparseMarkovAutomatonCslHelper::computeTotalRewards(
    Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix,
    storm::storage::SparseMatrix<double> const& backwardTransitions, std::vector<double> const& exitRateVector,
    storm::storage::BitVector const& markovianStates, storm::models::sparse::StandardRewardModel<double> const& rewardModel, bool produceScheduler);
 
template MDPSparseModelCheckingHelperReturnType<double> SparseMarkovAutomatonCslHelper::computeReachabilityTimes(
    Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<double> const& transitionMatrix,
    storm::storage::SparseMatrix<double> const& backwardTransitions, std::vector<double> const& exitRateVector,
    storm::storage::BitVector const& markovianStates, storm::storage::BitVector const& psiStates, bool produceScheduler);
 
template std::vector<storm::RationalNumber> SparseMarkovAutomatonCslHelper::computeBoundedUntilProbabilities(
    Environment const& env, storm::solver::SolveGoal<storm::RationalNumber>&& goal, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix,
    std::vector<storm::RationalNumber> const& exitRateVector, storm::storage::BitVector const& markovianStates, storm::storage::BitVector const& phiStates,
    storm::storage::BitVector const& psiStates, std::pair<double, std::optional<double>> const& boundsPair);
 
template MDPSparseModelCheckingHelperReturnType<storm::RationalNumber> SparseMarkovAutomatonCslHelper::computeUntilProbabilities(
    Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix,
    storm::storage::SparseMatrix<storm::RationalNumber> const& backwardTransitions, storm::storage::BitVector const& phiStates,
    storm::storage::BitVector const& psiStates, bool qualitative, bool produceScheduler);
 
template MDPSparseModelCheckingHelperReturnType<storm::RationalNumber> SparseMarkovAutomatonCslHelper::computeReachabilityRewards(
    Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix,
    storm::storage::SparseMatrix<storm::RationalNumber> const& backwardTransitions, std::vector<storm::RationalNumber> const& exitRateVector,
    storm::storage::BitVector const& markovianStates, storm::models::sparse::StandardRewardModel<storm::RationalNumber> const& rewardModel,
    storm::storage::BitVector const& psiStates, bool produceScheduler);
 
template MDPSparseModelCheckingHelperReturnType<storm::RationalNumber> SparseMarkovAutomatonCslHelper::computeTotalRewards(
    Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix,
    storm::storage::SparseMatrix<storm::RationalNumber> const& backwardTransitions, std::vector<storm::RationalNumber> const& exitRateVector,
    storm::storage::BitVector const& markovianStates, storm::models::sparse::StandardRewardModel<storm::RationalNumber> const& rewardModel,
    bool produceScheduler);
 
template MDPSparseModelCheckingHelperReturnType<storm::RationalNumber> SparseMarkovAutomatonCslHelper::computeReachabilityTimes(
    Environment const& env, OptimizationDirection dir, storm::storage::SparseMatrix<storm::RationalNumber> const& transitionMatrix,
    storm::storage::SparseMatrix<storm::RationalNumber> const& backwardTransitions, std::vector<storm::RationalNumber> const& exitRateVector,
    storm::storage::BitVector const& markovianStates, storm::storage::BitVector const& psiStates, bool produceScheduler);
}  // namespace helper
}  // namespace modelchecker
}  // namespace storm