Merge branch 'foxglynn'

src/storm/modelchecker/csl/helper/SparseCtmcCslHelper.cpp

@@ -632,19 +632,21 @@ namespace storm {
                 }
                 
                 // Use Fox-Glynn to get the truncation points and the weights.
-                std::tuple<uint_fast64_t, uint_fast64_t, ValueType, std::vector<ValueType>> foxGlynnResult = storm::utility::numerical::getFoxGlynnCutoff(lambda, 1e+300, storm::settings::getModule<storm::settings::modules::GeneralSettings>().getPrecision() / 8.0);
-                STORM_LOG_DEBUG("Fox-Glynn cutoff points: left=" << std::get<0>(foxGlynnResult) << ", right=" << std::get<1>(foxGlynnResult));
+//                std::tuple<uint_fast64_t, uint_fast64_t, ValueType, std::vector<ValueType>> foxGlynnResult = storm::utility::numerical::getFoxGlynnCutoff(lambda, 1e+300, storm::settings::getModule<storm::settings::modules::GeneralSettings>().getPrecision() / 8.0);
+                
+                storm::utility::numerical::FoxGlynnResult<ValueType> foxGlynnResult = storm::utility::numerical::foxGlynn(lambda, storm::settings::getModule<storm::settings::modules::GeneralSettings>().getPrecision() / 8.0);
+                STORM_LOG_DEBUG("Fox-Glynn cutoff points: left=" << foxGlynnResult.left << ", right=" << foxGlynnResult.right);
                 
                 // Scale the weights so they add up to one.
-                for (auto& element : std::get<3>(foxGlynnResult)) {
-                    element /= std::get<2>(foxGlynnResult);
+                for (auto& element : foxGlynnResult.weights) {
+                    element /= foxGlynnResult.totalWeight;
                 }
                 
                 // If the cumulative reward is to be computed, we need to adjust the weights.
                 if (useMixedPoissonProbabilities) {
                     ValueType sum = storm::utility::zero<ValueType>();
                     
-                    for (auto& element : std::get<3>(foxGlynnResult)) {
+                    for (auto& element : foxGlynnResult.weights) {
                         sum += element;
                         element = (1 - sum) / uniformizationRate;
                     }
@@ -654,10 +656,10 @@ namespace storm {
                 
                 // Initialize result.
                 std::vector<ValueType> result;
-                uint_fast64_t startingIteration = std::get<0>(foxGlynnResult);
+                uint_fast64_t startingIteration = foxGlynnResult.left;
                 if (startingIteration == 0) {
                     result = values;
-                    storm::utility::vector::scaleVectorInPlace(result, std::get<3>(foxGlynnResult)[0]);
+                    storm::utility::vector::scaleVectorInPlace(result, foxGlynnResult.weights.front());
                     ++startingIteration;
                 } else {
                     if (useMixedPoissonProbabilities) {
@@ -672,9 +674,9 @@ namespace storm {
                 std::unique_ptr<storm::solver::LinearEquationSolver<ValueType>> solver = linearEquationSolverFactory.create(env, std::move(uniformizedMatrix), storm::solver::LinearEquationSolverTask::Multiply);
                 solver->setCachingEnabled(true);
                 
-                if (!useMixedPoissonProbabilities && std::get<0>(foxGlynnResult) > 1) {
+                if (!useMixedPoissonProbabilities && foxGlynnResult.left > 1) {
                     // Perform the matrix-vector multiplications (without adding).
-                    solver->repeatedMultiply(values, addVector, std::get<0>(foxGlynnResult) - 1);
+                    solver->repeatedMultiply(values, addVector, foxGlynnResult.left - 1);
                 } else if (useMixedPoissonProbabilities) {
                     std::function<ValueType(ValueType const&, ValueType const&)> addAndScale = [&uniformizationRate] (ValueType const& a, ValueType const& b) { return a + b / uniformizationRate; };
                     
@@ -689,10 +691,10 @@ namespace storm {
                 // multiplication, scale and add the result.
                 ValueType weight = 0;
                 std::function<ValueType(ValueType const&, ValueType const&)> addAndScale = [&weight] (ValueType const& a, ValueType const& b) { return a + weight * b; };
-                for (uint_fast64_t index = startingIteration; index <= std::get<1>(foxGlynnResult); ++index) {
+                for (uint_fast64_t index = startingIteration; index <= foxGlynnResult.right; ++index) {
                     solver->repeatedMultiply(values, addVector, 1);
                     
-                    weight = std::get<3>(foxGlynnResult)[index - std::get<0>(foxGlynnResult)];
+                    weight = foxGlynnResult.weights[index - foxGlynnResult.left];
                     storm::utility::vector::applyPointwise(result, values, result, addAndScale);
                 }
                 

src/storm/utility/numerical.cpp

@@ -0,0 +1,275 @@
+#include "storm/utility/numerical.h"
+
+#include <cmath>
+#include <boost/math/constants/constants.hpp>
+
+#include "storm/utility/macros.h"
+#include "storm/utility/constants.h"
+#include "storm/exceptions/InvalidArgumentException.h"
+#include "storm/exceptions/PrecisionExceededException.h"
+
+namespace storm {
+    namespace utility {
+        namespace numerical {
+
+            template<typename ValueType>
+            FoxGlynnResult<ValueType>::FoxGlynnResult() : left(0), right(0), totalWeight(storm::utility::zero<ValueType>()) {
+                // Intentionally left empty.
+            }
+            
+            /*!
+             * The following implementation of Fox and Glynn's algorithm is taken from David Jansen's patched version
+             * in MRMC, which is based on his paper:
+             *
+             * https://pms.cs.ru.nl/iris-diglib/src/getContent.php?id=2011-Jansen-UnderstandingFoxGlynn
+             *
+             * We have only adapted the code to match more of C++'s and our coding guidelines.
+             */
+            
+            template<typename ValueType>
+            FoxGlynnResult<ValueType> foxGlynnFinder(ValueType lambda, ValueType epsilon) {
+                ValueType tau = std::numeric_limits<ValueType>::min();
+                ValueType omega = std::numeric_limits<ValueType>::max();
+                ValueType const sqrt_2_pi = boost::math::constants::root_two_pi<ValueType>();
+                ValueType const log10_e = std::log10(boost::math::constants::e<ValueType>());
+                
+                uint64_t m = static_cast<uint64_t>(lambda);
+                
+                int64_t left = 0;
+                int64_t right = 0;
+                
+                // tau is only used in underflow checks, which we are going to do in the logarithm domain.
+                tau = log(tau);
+                
+                // In error bound comparisons, we always compare with epsilon*sqrt_2_pi.
+                epsilon *= sqrt_2_pi;
+                
+                // Compute left truncation point.
+                if (m < 25) {
+                    // For lambda below 25 the exponential can be smaller than tau. If that is the case we expect
+                    // underflows and warn the user.
+                    if (-lambda <= tau) {
+                        STORM_LOG_WARN("Fox-Glynn: 0 < lambda < 25, underflow near Poi(" << lambda << ", 0) = " << std::exp(-lambda) << ". The results are unreliable.");
+                    }
+                    
+                    // Zero is used as left truncation point for lambda <= 25.
+                    left = 0;
+                } else {
+                    // Compute the left truncation point for lambda >= 25 (for lambda < 25 we use zero as left truncation point).
+
+                    ValueType const bl = (1 + 1 / lambda) * std::exp((1/lambda) * 0.125);
+                    ValueType const sqrt_lambda = std::sqrt(lambda);
+                    int64_t k;
+                    
+                    // Start looking for the left truncation point:
+                    // * start search at k=4 (taken from original Fox-Glynn paper)
+                    // * increase the left truncation point until we fulfil the error condition
+                    
+                    for (k = 4;; ++k) {
+                        ValueType max_err;
+                        
+                        left = m - static_cast<int64_t>(std::ceil(k*sqrt_lambda + 0.5));
+                        
+                        // For small lambda the above calculation can yield negative truncation points, crop them here.
+                        if (left <= 0) {
+                            left = 0;
+                            break;
+                        }
+                        
+                        // Note that Propositions 2-4 in Fox--Glynn mix up notation: they write Phi where they mean
+                        // 1 - Phi. (In Corollaries 1 and 2, phi is used correctly again.)
+                        max_err = bl * exp(-0.5 * (k*k)) / k;
+                        if (max_err * 2 <= epsilon) {
+                            // If the error on the left hand side is smaller, we can be more lenient on the right hand
+                            // side. To this end, we now set epsilon to the part of the error that has not yet been eaten
+                            // up by the left-hand truncation.
+                            epsilon -= max_err;
+                            break;
+                        }
+                    }
+                    
+                    // Finally the left truncation point is found.
+                }
+                
+                // Compute right truncation point.
+                {
+                    ValueType lambda_max;
+                    int64_t m_max, k;
+                    
+                    // According to Fox-Glynn, if lambda < 400 we should take lambda = 400, otherwise use the original
+                    // value. This is for computing the right truncation point.
+                    if (m < 400) {
+                        lambda_max = 400;
+                        m_max = 400;
+                        epsilon *= 0.662608824988162441697980;
+                        /* i.e. al = (1+1/400) * exp(1/16) * sqrt_2; epsilon /= al; */
+                    } else {
+                        lambda_max = lambda;
+                        m_max = m;
+                        epsilon *= (1 - 1 / (lambda + 1)) * 0.664265347050632847802225;
+                        /* i.e. al = (1+1/lambda) * exp(1/16) * sqrt_2; epsilon /= al; */
+                    }
+                    
+                    // Find right truncation point.
+                    
+                    // This loop is a modification to the original Fox-Glynn paper.
+                    // The search for the right truncation point is only terminated by  the error condition and not by
+                    // the stop index from the FG paper. This can yield more accurate results if necessary.
+                    for (k = 4;; ++k) {
+                        // dkl_inv is between 1 - 1e-33 and 1 if lambda_max >= 400 and k >= 4; this will always be
+                        // rounded to 1.0. We therefore leave the factor out.
+                        // double dkl_inv=1 - exp(-266/401.0 * (k*sqrt(2*lambda_max) + 1.5));
+                        
+                        // actually: "k * (dkl_inv*epsilon/al) >= exp(-0.5 * k^2)", but epsilon has been changed appropriately.
+                        if (k * epsilon >= exp(-0.5*(k*k))) {
+                            break;
+                        }
+                    }
+                    right = m_max + static_cast<int64_t>(std::ceil(k * std::sqrt(2 * lambda_max) + 0.5));
+                    if (right > m_max + static_cast<int64_t>(std::ceil((lambda_max + 1) * 0.5))) {
+                        STORM_LOG_WARN("Fox-Glynn: right = " << right << " >> lambda = " << lambda_max << ", cannot bound the right tail. The results are unreliable.");
+                    }
+                }
+                
+                // Time to set the initial value for weights.
+                FoxGlynnResult<ValueType> fgresult;
+                fgresult.left = static_cast<uint64_t>(left);
+                fgresult.right = static_cast<uint64_t>(right);
+                fgresult.weights.resize(fgresult.right - fgresult.left + 1);
+
+                fgresult.weights[m - left] = omega / (1.0e+10 * (right - left));
+                
+                if (m >= 25) {
+                    // Perform underflow check.
+                    ValueType result, log_c_m_inf;
+                    int64_t i;
+                    
+                    // we are going to compare with tau - log(w[m]).
+                    tau -= std::log(fgresult.weights[m - left]);
+                    
+                    // We take the c_m_inf = 0.14627 / sqrt( m ), as for lambda >= 25
+                    // c_m = 1 / ( sqrt( 2.0 * pi * m ) ) * exp( m - lambda - 1 / ( 12.0 * m ) ) => c_m_inf.
+                    // Note that m-lambda is in the interval (-1,0], and -1/(12*m) is in [-1/(12*25),0).
+                    // So, exp(m-lambda - 1/(12*m)) is in (exp(-1-1/(12*25)),exp(0)).
+                    // Therefore, we can improve the lower bound on c_m to exp(-1-1/(12*25)) / sqrt(2*pi) = ~0.14627.
+                    // Its logarithm is -1 - 1/(12*25) - log(2*pi) * 0.5 = ~ -1.922272 (rounded towards -infinity).
+                    log_c_m_inf = -1.922272 - log((double) m) * 0.5;
+                    
+                    // We use FG's Proposition 6 directly (and not Corollary 4 i and ii), as k_prime may be too large
+                    // if pFG->left == 0.
+                    i = m - left;
+                    
+                    // Equivalent to 2*i <= m, equivalent to i <= lambda/2.
+                    if (i <= left) {
+                        // Use Proposition 6 (i). Note that Fox--Glynn are off by one in the proof of this proposition;
+                        // they sum up to i-1, but should have summed up to i. */
+                        result = log_c_m_inf
+                        - i * (i+1) * (0.5 + (2*i+1)/(6*lambda)) / lambda;
+                    } else {
+                        // Use Corollary 4 (iii). Note that k_prime <= sqrt(m+1)/m is a misprint for k_prime <= m/sqrt(m+1),
+                        // which is equivalent to left >= 0, which holds trivially.
+                        result = -lambda;
+                        if (left != 0) {
+                            // Also use Proposition 6 (ii).
+                            double result_1 = log_c_m_inf + i * log(1 - i/(double) (m+1));
+                            
+                            // Take the maximum.
+                            if (result_1 > result) {
+                                result = result_1;
+                            }
+                        }
+                    }
+                    if (result <= tau) {
+                        int64_t const log10_result = static_cast<int64_t>(std::floor(result * log10_e));
+                        STORM_LOG_WARN("Fox-Glynn: lambda >= 25, underflow near Poi(" << lambda << "," << left << ") <= " << std::exp(result - log10_result/log10_e) << log10_result << ". The results are unreliable.");
+                    }
+                    
+                    // We still have to perform an underflow check for the right truncation point when lambda >= 400.
+                    if (m >= 400) {
+                        // Use Proposition 5 of Fox--Glynn.
+                        i = right - m;
+                        result = log_c_m_inf - i * (i + 1) / (2 * lambda);
+                        if (result <= tau) {
+                            int64_t const log10_result = static_cast<int64_t>(std::floor(result * log10_e));
+                            STORM_LOG_WARN("Fox-Glynn: lambda >= 25, underflow near Poi(" << lambda << "," << right << ") <= " << std::exp(result - log10_result/log10_e) << log10_result << ". The results are unreliable.");
+                        }
+                    }
+                }
+                
+                return fgresult;
+            }
+            
+            template<typename ValueType>
+            FoxGlynnResult<ValueType> foxGlynnWeighter(ValueType lambda, ValueType epsilon) {
+                ValueType tau = std::numeric_limits<ValueType>::min();
+
+                // The magic m point.
+                uint64_t m = static_cast<uint64_t>(lambda);
+                int64_t j, t;
+
+                FoxGlynnResult<ValueType> result = foxGlynnFinder(lambda, epsilon);
+                
+                // Fill the left side of the array.
+                for (j = m - result.left; j > 0; --j) {
+                    result.weights[j - 1] = (j + result.left) / lambda * result.weights[j];
+                }
+                
+                t = result.right - result.left;
+                
+                // Fill the right side of the array, have two cases lambda < 400 & lambda >= 400.
+                if (m < 400) {
+                    // Perform the underflow check, according to Fox-Glynn.
+                    STORM_LOG_THROW(result.right <= 600, storm::exceptions::PrecisionExceededException, "Fox-Glynn: " << result.right << " > 600, underflow is possible.");
+
+                    // Compute weights.
+                    for (j = m - result.left; j < t; ++j) {
+                        ValueType q = lambda / (j + 1 + result.left);
+                        if (result.weights[j] > tau / q) {
+                            result.weights[j + 1] = q * result.weights[j];
+                        } else {
+                            t = j;
+                            result.right = j + result.left;
+                            
+                            // It's time to compute W.
+                            break;
+                        }
+                    }
+                } else {
+                    // Compute weights.
+                    for (j = m - result.left; j < t; ++j) {
+                        result.weights[j + 1] = lambda / (j + 1 + result.left) * result.weights[j];
+                    }
+                }
+                
+                // It is time to compute the normalization weight W.
+                result.totalWeight = storm::utility::zero<ValueType>();
+                j = 0;
+                
+                // t was set above.
+                while(j < t) {
+                    if (result.weights[j] <= result.weights[t]) {
+                        result.totalWeight += result.weights[j];
+                        j++;
+                    } else {
+                        result.totalWeight += result.weights[t];
+                        t--;
+                    }
+                }
+                result.totalWeight += result.weights[j];
+                
+                STORM_LOG_TRACE("Fox-Glynn: ltp = " << result.left << ", rtp = " << result.right << ", w = " << result.totalWeight << ".");
+                
+                return result;
+            }
+            
+            template<typename ValueType>
+            FoxGlynnResult<ValueType> foxGlynn(ValueType lambda, ValueType epsilon) {
+                STORM_LOG_THROW(lambda > 0, storm::exceptions::InvalidArgumentException, "Fox-Glynn requires positive lambda.");
+                return foxGlynnWeighter(lambda, epsilon);
+            }
+
+            template FoxGlynnResult<double> foxGlynn(double lambda, double epsilon);
+            
+        }
+    }
+}

src/storm/utility/numerical.h

@@ -1,131 +1,23 @@
 #include <vector>
-#include <tuple>
-#include <cmath>
-
-#include "storm/utility/macros.h"
-#include "storm/utility/constants.h"
-#include "storm/exceptions/InvalidArgumentException.h"
-#include "storm/exceptions/OutOfRangeException.h"
+#include <cstdint>
 
 namespace storm {
     namespace utility {
         namespace numerical {
-            template<typename ValueType>
-            std::tuple<uint_fast64_t, uint_fast64_t, ValueType, std::vector<ValueType>> getFoxGlynnCutoff(ValueType lambda, ValueType overflow, ValueType accuracy) {
-                STORM_LOG_THROW(lambda != storm::utility::zero<ValueType>(), storm::exceptions::InvalidArgumentException, "Error in Fox-Glynn algorithm: lambda must not be zero.");
-                
-                // This code is a modified version of the one in PRISM. According to their implementation, for lambda
-                // smaller than 400, we compute the result using the naive method.
-                if (lambda < 400) {
-                    ValueType eToPowerMinusLambda = std::exp(-lambda);
-                    ValueType targetValue = (1 - accuracy) / eToPowerMinusLambda;
-                    std::vector<ValueType> weights;
-                    
-                    ValueType exactlyKEvents = 1;
-                    ValueType atMostKEvents = exactlyKEvents;
-                    weights.push_back(exactlyKEvents * eToPowerMinusLambda);
-                    
-                    uint_fast64_t k = 1;
-                    do {
-                        exactlyKEvents *= lambda / k;
-                        atMostKEvents += exactlyKEvents;
-                        weights.push_back(exactlyKEvents * eToPowerMinusLambda);
-                        ++k;
-                    } while (atMostKEvents < targetValue);
-                    
-                    return std::make_tuple(0, k - 1, 1.0, weights);
-                } else {
-                    STORM_LOG_THROW(accuracy >= 1e-10, storm::exceptions::InvalidArgumentException, "Error in Fox-Glynn algorithm: the accuracy must not be below 1e-10.");
-                    
-                    // Factor from Fox&Glynn's paper. The paper does not explain where it comes from.
-                    ValueType factor = 1e+10;
-                    
-                    // Now start the Finder algorithm to find the truncation points.
-                    ValueType m = std::floor(lambda);
-                    uint_fast64_t leftTruncationPoint = 0, rightTruncationPoint = 0;
-                    {
-                        // Factors used by the corollaries explained in Fox & Glynns paper.
-                        // Square root of pi.
-                        ValueType sqrtpi = 1.77245385090551602729;
-                        
-                        // Square root of 2.
-                        ValueType sqrt2 = 1.41421356237309504880;
-                        
-                        // Set up a_\lambda, b_\lambda, and the square root of lambda.
-                        ValueType aLambda = 0, bLambda = 0, sqrtLambda = 0;
-                        if (m < 400) {
-                            sqrtLambda = std::sqrt(400.0);
-                            aLambda = (1.0 + 1.0 / 400.0) * std::exp(0.0625) * sqrt2;
-                            bLambda = (1.0 + 1.0 / 400.0) * std::exp(0.125 / 400.0);
-                        } else {
-                            sqrtLambda = std::sqrt(lambda);
-                            aLambda = (1.0 + 1.0 / lambda) * std::exp(0.0625) * sqrt2;
-                            bLambda = (1.0 + 1.0 / lambda) * std::exp(0.125 / lambda);
-                        }
-                        
-                        // Use Corollary 1 from the paper to find the right truncation point.
-                        uint_fast64_t k = 4;
-                        
-                        ValueType dkl = 1.0 / (1 - std::exp(-(2.0 / 9.0) * (k * sqrt2 * sqrtLambda + 1.5)));
 
-                        // According to David Jansen the upper bound can be ignored to achieve more accurate results.
-                        // Right hand side of the equation in Corollary 1.
-                        while ((accuracy / 2.0) < (aLambda * dkl * std::exp(-(k*k / 2.0)) / (k * sqrt2 * sqrtpi))) {
-                            ++k;
-                            
-                            // d(k,Lambda) from the paper.
-                            dkl = 1.0 / (1 - std::exp(-(2.0 / 9.0)*(k * sqrt2 * sqrtLambda + 1.5)));
-                        }
-                        
-                        // Left hand side of the equation in Corollary 1.
-                        rightTruncationPoint = static_cast<uint_fast64_t>(std::ceil((m + k * sqrt2 * sqrtLambda + 1.5)));
-                        
-                        // Use Corollary 2 to find left truncation point.
-                        k = 4;
-                        
-                        // Right hand side of the equation in Corollary 2.
-                        while ((accuracy / 2.0) < ((bLambda * std::exp(-(k*k / 2.0))) / (k * sqrt2 * sqrtpi))) {
-                            ++k;
-                        }
-                        
-                        // Left hand side of the equation in Corollary 2.
-                        leftTruncationPoint = static_cast<uint_fast64_t>(std::max(std::trunc(m - k * sqrtLambda - 1.5), storm::utility::zero<ValueType>()));
-                        
-                        // Check for underflow.
-                        STORM_LOG_THROW(std::trunc((m - k * sqrtLambda - 1.5)) >= 0, storm::exceptions::OutOfRangeException, "Error in Fox-Glynn algorithm: Underflow of left truncation point.");
-                    }
-                    
-                    std::vector<ValueType> weights(rightTruncationPoint - leftTruncationPoint + 1);
-                    weights[m - leftTruncationPoint] = overflow / (factor * (rightTruncationPoint - leftTruncationPoint));
-                    for (uint_fast64_t j = m; j > leftTruncationPoint; --j) {
-                        weights[j - 1 - leftTruncationPoint] = (j / lambda) * weights[j - leftTruncationPoint];
-                    }
-                    
-                    for (uint_fast64_t j = m; j < rightTruncationPoint; ++j) {
-                        weights[j + 1 - leftTruncationPoint] = (lambda / (j + 1)) * weights[j - leftTruncationPoint];
-                    }
-                    
-                    
-                    // Compute the total weight and start with smallest to avoid roundoff errors.
-                    ValueType totalWeight = storm::utility::zero<ValueType>();
+            template<typename ValueType>
+            struct FoxGlynnResult {
+                FoxGlynnResult();
 
-                    uint_fast64_t s = leftTruncationPoint;
-                    uint_fast64_t t = rightTruncationPoint;
-                    while (s < t) {
-                        if (weights[s - leftTruncationPoint] <= weights[t - leftTruncationPoint]) {
-                            totalWeight += weights[s - leftTruncationPoint];
-                            ++s;
-                        } else {
-                            totalWeight += weights[t - leftTruncationPoint];
-                            --t;
-                        }
-                    }
+                uint64_t left;
+                uint64_t right;
+                ValueType totalWeight;
+                std::vector<ValueType> weights;
+            };
             
-                    totalWeight += weights[s - leftTruncationPoint];
+            template<typename ValueType>
+            FoxGlynnResult<ValueType> foxGlynn(ValueType lambda, ValueType epsilon);
                 
-                    return std::make_tuple(leftTruncationPoint, rightTruncationPoint, totalWeight, weights);
-                }
-            }
         }
     }
 }