early termination

Former-commit-id: a1752a6fb7

src/modelchecker/region/ApproximationModel.cpp

@@ -12,6 +12,7 @@
 #include "src/models/sparse/Mdp.h"
 #include "src/models/ModelType.h"
 #include "src/models/sparse/StandardRewardModel.h"
+#include "src/solver/AllowEarlyTerminationCondition.h"
 #include "src/solver/MinMaxLinearEquationSolver.h"
 #include "src/solver/GameSolver.h"
 #include "src/utility/macros.h"
@@ -42,7 +43,6 @@ namespace storm {
                 } else {
                     STORM_LOG_THROW(false, storm::exceptions::InvalidArgumentException, "Invalid formula: " << formula << ". Approximation model only supports eventually or reachability reward formulae.");
                 }
-                this->solverData.player1Goal = storm::solver::SolveGoal(storm::logic::isLowerBound(formula->getComparisonType()));
                 STORM_LOG_THROW(parametricModel.hasLabel("target"), storm::exceptions::InvalidArgumentException, "The given Model has no \"target\"-statelabel.");
                 this->targetStates = parametricModel.getStateLabeling().getStates("target");
                 STORM_LOG_THROW(parametricModel.hasLabel("sink"), storm::exceptions::InvalidArgumentException, "The given Model has no \"sink\"-statelabel.");
@@ -76,6 +76,15 @@ namespace storm {
                 this->solverData.initialStateIndex = newIndices[initialState];
                 this->solverData.lastMinimizingPolicy = Policy(this->matrixData.matrix.getRowGroupCount(), 0);
                 this->solverData.lastMaximizingPolicy = Policy(this->matrixData.matrix.getRowGroupCount(), 0);
+                //this->solverData.player1Goal = storm::solver::SolveGoal(storm::logic::isLowerBound(formula->getComparisonType()));
+                storm::storage::BitVector filter(this->solverData.result.size(), false);
+                filter.set(this->solverData.initialStateIndex, true);
+                this->solverData.player1Goal = std::make_unique<storm::solver::BoundedGoal<ConstantType>>(
+                            storm::logic::isLowerBound(formula->getComparisonType()) ? storm::solver::OptimizationDirection::Minimize : storm::solver::OptimizationDirection::Maximize,
+                            formula->getComparisonType(),
+                            formula->getBound(),
+                            filter
+                        );
             }                
 
             template<typename ParametricSparseModelType, typename ConstantType>
@@ -398,11 +407,18 @@ namespace storm {
             
                         
             template<typename ParametricSparseModelType, typename ConstantType>
-            void ApproximationModel<ParametricSparseModelType, ConstantType>::invokeSolver(bool computeLowerBounds, Policy& policy){
+            void ApproximationModel<ParametricSparseModelType, ConstantType>::invokeSolver(bool computeLowerBounds, Policy& policy, bool allowEarlyTermination){
                 storm::solver::SolveGoal player2Goal(computeLowerBounds);
                 if(this->typeOfParametricModel == storm::models::ModelType::Dtmc){
                     //Invoke mdp model checking
                     std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ConstantType>> solver = storm::solver::configureMinMaxLinearEquationSolver(player2Goal, storm::utility::solver::MinMaxLinearEquationSolverFactory<double>(), this->matrixData.matrix);
+                    if(allowEarlyTermination){
+                        solver->setEarlyTerminationCriterion(std::make_unique<storm::solver::TerminateAfterFilteredExtremumBelowOrAboveThreshold<ConstantType>>(
+                                    this->solverData.player1Goal->relevantColumns(),
+                                    this->solverData.player1Goal->thresholdValue(),
+                                    computeLowerBounds
+                                ));
+                    }
                     storm::utility::policyguessing::solveMinMaxLinearEquationSystem(*solver,
                                 this->solverData.result, this->vectorData.vector,
                                 player2Goal.direction(),
@@ -412,9 +428,17 @@ namespace storm {
                 } else if(this->typeOfParametricModel == storm::models::ModelType::Mdp){
                     //Invoke stochastic two player game model checking
                     std::unique_ptr<storm::solver::GameSolver<ConstantType>> solver = storm::utility::solver::GameSolverFactory<ConstantType>().create(this->solverData.player1Matrix, this->matrixData.matrix);
+                    if(allowEarlyTermination && this->solverData.player1Goal->minimize() == computeLowerBounds){
+                        //We play MIN-Min or Max-Max
+                    solver->setEarlyTerminationCriterion(std::make_unique<storm::solver::TerminateAfterFilteredExtremumBelowOrAboveThreshold<ConstantType>>(
+                                this->solverData.player1Goal->relevantColumnVector,
+                                this->solverData.player1Goal->thresholdValue(),
+                                computeLowerBounds
+                            ));
+                    }
                     storm::utility::policyguessing::solveGame(*solver, 
                                 this->solverData.result, this->vectorData.vector,
-                                this->solverData.player1Goal.direction(), player2Goal.direction(), 
+                                this->solverData.player1Goal->direction(), player2Goal.direction(), 
                                 this->solverData.lastPlayer1Policy, policy,
                                 this->matrixData.targetChoices, (this->computeRewards ? storm::utility::infinity<ConstantType>() : storm::utility::zero<ConstantType>())
                         );

src/modelchecker/region/ApproximationModel.h

@@ -76,7 +76,7 @@ namespace storm {
                 void initializeRewards(ParametricSparseModelType const& parametricModel, std::vector<std::size_t> const& newIndices);
                 void initializePlayer1Matrix(ParametricSparseModelType const& parametricModel);
                 void instantiate(ParameterRegion<ParametricType> const& region, bool computeLowerBounds);
-                void invokeSolver(bool computeLowerBounds, Policy& policy);
+                void invokeSolver(bool computeLowerBounds, Policy& policy, bool allowEarlyTermination=true);
 
                 //A flag that denotes whether we compute probabilities or rewards
                 bool computeRewards;
@@ -92,7 +92,7 @@ namespace storm {
                     Policy lastMinimizingPolicy, lastMaximizingPolicy;
                     std::size_t initialStateIndex; //The index which represents the result for the initial state in the result vector
                     //Player 1 represents the nondeterminism of the given mdp (so, this is irrelevant if we approximate values of a DTMC)
-                    storm::solver::SolveGoal player1Goal = storm::solver::SolveGoal(true); //No default cunstructor for solve goal...
+                    std::unique_ptr<storm::solver::BoundedGoal<ConstantType>> player1Goal;
                     storm::storage::SparseMatrix<storm::storage::sparse::state_type> player1Matrix;
                     Policy lastPlayer1Policy;
                 } solverData;

src/modelchecker/region/SamplingModel.cpp

@@ -20,7 +20,6 @@
 #include "src/utility/policyguessing.h"
 #include "src/exceptions/UnexpectedException.h"
 #include "src/exceptions/InvalidArgumentException.h"
-#include "storage/dd/CuddBdd.h"
 
 namespace storm {
     namespace modelchecker {
@@ -41,7 +40,6 @@ namespace storm {
                 } else {
                     STORM_LOG_THROW(false, storm::exceptions::InvalidArgumentException, "Invalid formula: " << formula << ". Sampling model only supports eventually or reachability reward formulae.");
                 }
-                this->solverData.solveGoal = storm::solver::SolveGoal(storm::logic::isLowerBound(formula->getComparisonType()));
                 STORM_LOG_THROW(parametricModel.hasLabel("target"), storm::exceptions::InvalidArgumentException, "The given Model has no \"target\"-statelabel.");
                 this->targetStates = parametricModel.getStateLabeling().getStates("target");
                 STORM_LOG_THROW(parametricModel.hasLabel("sink"), storm::exceptions::InvalidArgumentException, "The given Model has no \"sink\"-statelabel.");
@@ -70,6 +68,16 @@ namespace storm {
                 this->solverData.result = std::vector<ConstantType>(maybeStates.getNumberOfSetBits(), this->computeRewards ? storm::utility::one<ConstantType>() : ConstantType(0.5));
                 this->solverData.initialStateIndex = newIndices[initialState];
                 this->solverData.lastPolicy = Policy(this->matrixData.matrix.getRowGroupCount(), 0);
+                
+                //this->solverData.solveGoal = storm::solver::SolveGoal(storm::logic::isLowerBound(formula->getComparisonType()));
+                storm::storage::BitVector filter(this->solverData.result.size(), false);
+                filter.set(this->solverData.initialStateIndex, true);
+                this->solverData.solveGoal = std::make_unique<storm::solver::BoundedGoal<ConstantType>>(
+                            storm::logic::isLowerBound(formula->getComparisonType()) ? storm::solver::OptimizationDirection::Minimize : storm::solver::OptimizationDirection::Maximize,
+                            formula->getComparisonType(),
+                            formula->getBound(),
+                            filter
+                        );
             }
             
             template<typename ParametricSparseModelType, typename ConstantType>
@@ -253,17 +261,25 @@ namespace storm {
             }
             
             template<typename ParametricSparseModelType, typename ConstantType>
-            void SamplingModel<ParametricSparseModelType, ConstantType>::invokeSolver(){
+            void SamplingModel<ParametricSparseModelType, ConstantType>::invokeSolver(bool allowEarlyTermination){
                 if(this->typeOfParametricModel == storm::models::ModelType::Dtmc){
-                    std::unique_ptr<storm::solver::LinearEquationSolver<double>> solver = storm::utility::solver::LinearEquationSolverFactory<double>().create(this->matrixData.matrix);
+                    std::unique_ptr<storm::solver::LinearEquationSolver<ConstantType>> solver = storm::utility::solver::LinearEquationSolverFactory<ConstantType>().create(this->matrixData.matrix);
                     solver->solveEquationSystem(this->solverData.result, this->vectorData.vector);
                 } else if(this->typeOfParametricModel == storm::models::ModelType::Mdp){
-                    std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<double>> solver = storm::solver::configureMinMaxLinearEquationSolver(this->solverData.solveGoal, storm::utility::solver::MinMaxLinearEquationSolverFactory<double>(), this->matrixData.matrix);
+                    std::unique_ptr<storm::solver::MinMaxLinearEquationSolver<ConstantType>> solver = storm::utility::solver::MinMaxLinearEquationSolverFactory<ConstantType>().create(this->matrixData.matrix);
+                    solver->setOptimizationDirection(this->solverData.solveGoal->direction());
+                    if(allowEarlyTermination){
+                        solver->setEarlyTerminationCriterion(std::make_unique<storm::solver::TerminateAfterFilteredExtremumBelowOrAboveThreshold<ConstantType>>(
+                                    this->solverData.solveGoal->relevantColumns(),
+                                    this->solverData.solveGoal->thresholdValue(),
+                                    this->solverData.solveGoal->minimize()
+                                ));
+                    }
                     storm::utility::policyguessing::solveMinMaxLinearEquationSystem(*solver,
                                         this->solverData.result, this->vectorData.vector,
-                                        this->solverData.solveGoal.direction(),
+                                        this->solverData.solveGoal->direction(),
                                         this->solverData.lastPolicy,
-                                        this->matrixData.targetChoices, (this->computeRewards ? storm::utility::infinity<double>() : storm::utility::zero<double>()));
+                                        this->matrixData.targetChoices, (this->computeRewards ? storm::utility::infinity<ConstantType>() : storm::utility::zero<ConstantType>()));
                 } else {
                     STORM_LOG_THROW(false, storm::exceptions::UnexpectedException, "Unexpected Type of model");
                 }

src/modelchecker/region/SamplingModel.h

@@ -57,7 +57,7 @@ namespace storm {
                 void initializeProbabilities(ParametricSparseModelType const& parametricModel, std::vector<std::size_t> const& newIndices);
                 void initializeRewards(ParametricSparseModelType const& parametricModel, std::vector<std::size_t> const& newIndices);
                 void instantiate(std::map<VariableType, CoefficientType>const& point);
-                void invokeSolver();
+                void invokeSolver(bool allowEarlyTermination=true);
                 
                 //A flag that denotes whether we compute probabilities or rewards
                 bool computeRewards;
@@ -72,7 +72,7 @@ namespace storm {
                     std::vector<ConstantType> result; //Note: result.size==maybeStates.numberOfSetBits
                     std::size_t initialStateIndex; //The index which represents the result for the initial state in the result vector
                     //The following is only relevant if we consider mdps:
-                    storm::solver::SolveGoal solveGoal = storm::solver::SolveGoal(true); //No default cunstructor for solve goal...
+                    std::unique_ptr<storm::solver::BoundedGoal<ConstantType>> solveGoal;
                     Policy lastPolicy; //best policy from the previous instantiation. Serves as first guess for the next call.
                 } solverData;
                 

src/solver/AllowEarlyTerminationCondition.cpp

@@ -35,13 +35,34 @@ namespace storm {
             ValueType initVal = terminationThreshold - 1;
             ValueType currentThreshold = useMinimumAsExtremum ? storm::utility::vector::max_if(currentValues, filter, initVal) : storm::utility::vector::max_if(currentValues, filter, initVal);
             
-               return currentThreshold >= this->terminationThreshold;
+            return currentThreshold >= this->terminationThreshold;
+        }
+        
+        template<typename ValueType>
+        TerminateAfterFilteredExtremumBelowOrAboveThreshold<ValueType>::TerminateAfterFilteredExtremumBelowOrAboveThreshold(storm::storage::BitVector const& filter, ValueType threshold,  bool useMinimum) :
+        terminationThreshold(threshold), filter(filter), useMinimumAsExtremum(useMinimum)
+        {
+            // Intentionally left empty.
+        }
 
+        template<typename ValueType>
+        bool TerminateAfterFilteredExtremumBelowOrAboveThreshold<ValueType>::terminateNow(const std::vector<ValueType>& currentValues) const {
+            assert(currentValues.size() >= filter.size());
             
+            if(useMinimumAsExtremum){
+                ValueType initVal = terminationThreshold + 1;
+                ValueType currentThreshold = storm::utility::vector::min_if(currentValues, filter, initVal);
+                return currentThreshold < this->terminationThreshold;
+            } else {
+                ValueType initVal = terminationThreshold - 1;
+                ValueType currentThreshold = storm::utility::vector::max_if(currentValues, filter, initVal);
+                return currentThreshold > this->terminationThreshold;
+            }
         }
         
         
         template class TerminateAfterFilteredExtremumPassesThresholdValue<double>;
+        template class TerminateAfterFilteredExtremumBelowOrAboveThreshold<double>;
         template class TerminateAfterFilteredSumPassesThresholdValue<double>;
                 
     }

src/solver/AllowEarlyTerminationCondition.h

@@ -41,6 +41,18 @@ namespace storm {
             storm::storage::BitVector filter;
             bool useMinimumAsExtremum;
         };
+        
+        template<typename ValueType>
+        class TerminateAfterFilteredExtremumBelowOrAboveThreshold :  public AllowEarlyTerminationCondition<ValueType>{
+        public:
+            TerminateAfterFilteredExtremumBelowOrAboveThreshold(storm::storage::BitVector const& filter, ValueType threshold, bool useMinimum);
+            bool terminateNow(std::vector<ValueType> const& currentValue) const;
+            
+        protected:
+            ValueType terminationThreshold;
+            storm::storage::BitVector filter;
+            bool useMinimumAsExtremum;
+        };
     }
 }
 

src/solver/GameSolver.cpp

@@ -9,12 +9,12 @@
 namespace storm {
     namespace solver {
         template <typename ValueType>
-        GameSolver<ValueType>::GameSolver(storm::storage::SparseMatrix<storm::storage::sparse::state_type> const& player1Matrix, storm::storage::SparseMatrix<ValueType> const& player2Matrix) : AbstractGameSolver(), player1Matrix(player1Matrix), player2Matrix(player2Matrix) {
+        GameSolver<ValueType>::GameSolver(storm::storage::SparseMatrix<storm::storage::sparse::state_type> const& player1Matrix, storm::storage::SparseMatrix<ValueType> const& player2Matrix) : AbstractGameSolver(), player1Matrix(player1Matrix), player2Matrix(player2Matrix), earlyTermination(new NoEarlyTerminationCondition<ValueType>()) {
             // Intentionally left empty.
         }
 
         template <typename ValueType>
-        GameSolver<ValueType>::GameSolver(storm::storage::SparseMatrix<storm::storage::sparse::state_type> const& player1Matrix, storm::storage::SparseMatrix<ValueType> const& player2Matrix, double precision, uint_fast64_t maximalNumberOfIterations, bool relative) : AbstractGameSolver(precision, maximalNumberOfIterations, relative), player1Matrix(player1Matrix), player2Matrix(player2Matrix) {
+        GameSolver<ValueType>::GameSolver(storm::storage::SparseMatrix<storm::storage::sparse::state_type> const& player1Matrix, storm::storage::SparseMatrix<ValueType> const& player2Matrix, double precision, uint_fast64_t maximalNumberOfIterations, bool relative) : AbstractGameSolver(precision, maximalNumberOfIterations, relative), player1Matrix(player1Matrix), player2Matrix(player2Matrix) , earlyTermination(new NoEarlyTerminationCondition<ValueType>()) {
             // Intentionally left empty.
         }
 
@@ -69,9 +69,9 @@ namespace storm {
                 std::swap(x, tmpResult);
 
                 ++iterations;
-            } while (!converged && iterations < maximalNumberOfIterations);
+            } while (!converged && iterations < maximalNumberOfIterations && !this->earlyTermination->terminateNow(x));
             
-            STORM_LOG_WARN_COND(converged, "Iterative solver for stochastic two player games did not converge after " << iterations << " iterations.");
+            STORM_LOG_WARN_COND(converged || this->earlyTermination->terminateNow(x), "Iterative solver for stochastic two player games did not converge after " << iterations << " iterations.");
             
             if(this->trackPolicies){
                 this->player2Policy = std::vector<storm::storage::sparse::state_type>(player2Matrix.getRowGroupCount());
@@ -110,6 +110,11 @@ namespace storm {
             }
         }
         
+        template <typename ValueType>
+        void GameSolver<ValueType>::setEarlyTerminationCriterion(std::unique_ptr<AllowEarlyTerminationCondition<ValueType>> v) {
+                earlyTermination = std::move(v);
+        }
+
         template <typename ValueType>
         storm::storage::SparseMatrix<storm::storage::sparse::state_type> const& GameSolver<ValueType>::getPlayer1Matrix() const {
             return player1Matrix;

src/solver/GameSolver.h

@@ -4,6 +4,7 @@
 #include <vector>
 
 #include "src/solver/AbstractGameSolver.h"
+#include "src/solver/AllowEarlyTerminationCondition.h"
 #include "src/solver/OptimizationDirection.h"
 
 #include "src/storage/sparse/StateType.h"
@@ -53,6 +54,8 @@ namespace storm {
             storm::storage::SparseMatrix<ValueType> const& getPlayer2Matrix() const;
             storm::storage::SparseMatrix<storm::storage::sparse::state_type> const& getPlayer1Matrix() const;
 
+            void setEarlyTerminationCriterion(std::unique_ptr<AllowEarlyTerminationCondition<ValueType>> v);
+            
         private:
             // The matrix defining the choices of player 1.
             storm::storage::SparseMatrix<storm::storage::sparse::state_type> const& player1Matrix;
@@ -60,6 +63,9 @@ namespace storm {
             // The matrix defining the choices of player 2.
             storm::storage::SparseMatrix<ValueType> const& player2Matrix;
             
+            
+            std::unique_ptr<AllowEarlyTerminationCondition<ValueType>> earlyTermination;
+            
         };
     }
 }

src/solver/GmmxxMinMaxLinearEquationSolver.cpp

@@ -74,6 +74,8 @@ namespace storm {
 				// Check if the solver converged and issue a warning otherwise.
 				if (converged) {
 					LOG4CPLUS_INFO(logger, "Iterative solver converged after " << iterations << " iterations.");
+				} else if (this->earlyTermination->terminateNow(*currentX)) {
+					LOG4CPLUS_INFO(logger, "Iterative solver stopped due to early termination. Performed " << iterations << " iterations.");
 				} else {
 					LOG4CPLUS_WARN(logger, "Iterative solver did not converge after " << iterations << " iterations.");
 				}
@@ -85,6 +87,10 @@ namespace storm {
 				}
                                 
                                 if(this->trackPolicy){
+                                    if(iterations==0){ //may happen due to early termination. Then we need to compute x'= A*x+b
+					gmm::mult(*gmmxxMatrix, x, *multiplyResult);
+					gmm::add(b, *multiplyResult);
+                                    }
                                     this->policy = std::vector<storm::storage::sparse::state_type>(x.size());
                                     storm::utility::vector::reduceVectorMinOrMax(dir, *multiplyResult, x, rowGroupIndices, &(this->policy));
                                 }

src/solver/NativeMinMaxLinearEquationSolver.cpp

@@ -74,6 +74,8 @@ namespace storm {
 				// Check if the solver converged and issue a warning otherwise.
 				if (converged) {
 					LOG4CPLUS_INFO(logger, "Iterative solver converged after " << iterations << " iterations.");
+                                } else if (this->earlyTermination->terminateNow(*currentX)) {
+					LOG4CPLUS_INFO(logger, "Iterative solver stopped due to early termination. Performed " << iterations << " iterations.");
 				} else {
 					LOG4CPLUS_WARN(logger, "Iterative solver did not converge after " << iterations << " iterations.");
 				}
@@ -85,6 +87,10 @@ namespace storm {
 				}
 
                                 if(this->trackPolicy){
+                                    if(iterations==0){ //may happen due to early termination. Then we need to compute x'= A*x+b
+                                        this->A.multiplyWithVector(x, *multiplyResult);
+					storm::utility::vector::addVectors(*multiplyResult, b, *multiplyResult);
+                                    }
                                     this->policy = std::vector<storm::storage::sparse::state_type>(x.size());
                                     storm::utility::vector::reduceVectorMinOrMax(dir, *multiplyResult, x, this->A.getRowGroupIndices(), &(this->policy));
                                 }

src/utility/policyguessing.cpp

@@ -56,42 +56,6 @@ namespace storm {
                         //x = std::vector<ValueType>(x.size(), storm::utility::zero<ValueType>());
                     }
                 }                
-                /*
-                        std::size_t p2Precount=0;
-                        std::size_t p2Postcount=0;
-                        std::size_t p1diff =0;
-                        std::size_t p2diff =0;
-                        std::size_t p2RelevantCount=0;
-                        storm::storage::BitVector relevantP2Groups(pl2Policy.size(),false);
-                        for(std::size_t i = 0; i<pl1Policy.size(); ++i){
-                            if(pl1Policy[i] != player1Policy[i]){
-                                ++p1diff;
-                            }
-                            std::size_t row = solver.getPlayer1Matrix().getRowGroupIndices()[i] + player1Policy[i];
-                            auto rowObj = solver.getPlayer1Matrix().getRow(row);
-                            relevantP2Groups.set(rowObj.begin()->getColumn());
-                        }
-                        for (std::size_t i : relevantP2Groups){
-                            if(pl2Policy[i] != player2Policy[i]){
-                                ++p2RelevantCount;
-                            }
-                        }
-                        for(std::size_t i = 0; i<pl2Policy.size(); ++i){
-                            if(pl2Policy[i] != player2Policy[i]){
-                                ++p2diff;
-                            }
-                            p2Precount += pl2Policy[i];
-                            p2Postcount += player2Policy[i];
-                        }
-                        std::cout << "P1: " << (player1Goal == OptimizationDirection::Minimize ? "MIN " : "MAX ");
-                        std::cout << "P2: " << (player2Goal == OptimizationDirection::Minimize ? "MIN " : "MAX ");
-                        std::cout << "Changes: P1: " << p1diff;
-                        std::cout << " P2: " << p2diff << " (" << p2RelevantCount << " relevant)";
-                        std::cout << " Counts P2: " << p2Precount << " and " << p2Postcount << ".";
-                        std::cout << std::endl;
-                  */      
-                
-                
             }
             
             template <typename ValueType>
@@ -209,20 +173,22 @@ namespace storm {
                 storm::utility::vector::selectVectorValues(subB, probGreater0States, b);
                 std::unique_ptr<storm::solver::LinearEquationSolver<ValueType>> linEqSysSolver = storm::utility::solver::LinearEquationSolverFactory<ValueType>().create(eqSysA);
                 linEqSysSolver->setRelative(relative);
-                
+                linEqSysSolver->setIterations(500);
                 std::size_t iterations = 0;
                 std::vector<ValueType> copyX(subX.size());
                 ValueType newPrecision = precision;
                 do {
                     linEqSysSolver->setPrecision(newPrecision);
                     if(!linEqSysSolver->solveEquationSystem(subX, subB)){
-                        break; //Solver did not converge.. so we have to go on with the current solution.
+              //          break; //Solver did not converge.. so we have to go on with the current solution.
                     }
                     subA.multiplyWithVector(subX,copyX);
                     storm::utility::vector::addVectors(copyX, subB, copyX); // = Ax + b
                     ++iterations;
-                    newPrecision *= 0.1;
-                } while(!storm::utility::vector::equalModuloPrecision(subX, copyX, precision, relative) && iterations<30);
+                    newPrecision *= 0.5;
+                } while(!storm::utility::vector::equalModuloPrecision(subX, copyX, precision*0.5, relative) && iterations<50);
+                
+                STORM_LOG_WARN_COND(iterations<50, "Solving linear equation system did not yield a precise result");
                 
                 STORM_LOG_DEBUG("Required to increase the precision " << iterations << " times in order to obtain a precise result");
                 //fill in the result