Warning, file /education/step/stepcore/eulersolver.cc was not indexed or was modified since last indexation (in which case cross-reference links may be missing, inaccurate or erroneous).

 /*
     SPDX-FileCopyrightText: 2007 Vladimir Kuznetsov <ks.vladimir@gmail.com>
 
     SPDX-License-Identifier: GPL-2.0-or-later
 */
 
 #include "eulersolver.h"
 #include "util.h"
 
 #include <cmath>
 #include <cstring>
 
 namespace StepCore {
 
 STEPCORE_META_OBJECT(GenericEulerSolver, QT_TRANSLATE_NOOP("ObjectClass", "GenericEulerSolver"), QT_TRANSLATE_NOOP("ObjectDescription", "Generic Euler solver"), MetaObject::ABSTRACT, STEPCORE_SUPER_CLASS(Solver),)
 STEPCORE_META_OBJECT(EulerSolver, QT_TRANSLATE_NOOP("ObjectClass", "EulerSolver"), QT_TRANSLATE_NOOP("ObjectDescription", "Non-adaptive Euler solver"), 0, STEPCORE_SUPER_CLASS(GenericEulerSolver),)
 STEPCORE_META_OBJECT(AdaptiveEulerSolver, QT_TRANSLATE_NOOP("ObjectClass", "AdaptiveEulerSolver"), QT_TRANSLATE_NOOP("ObjectDescription", "Adaptive Euler solver"), 0, STEPCORE_SUPER_CLASS(GenericEulerSolver),)
 
 void GenericEulerSolver::init()
 {
     _yerr.resize(_dimension);
     _ytemp.resize(_dimension);
     _ydiff.resize(_dimension);
     _ytempvar.resize(_dimension);
     _ydiffvar.resize(_dimension);
 }
 
 void GenericEulerSolver::fini()
 {
 }
 
 int GenericEulerSolver::doCalcFn(double* t, const VectorXd* y,
                     const VectorXd* yvar, VectorXd* f, VectorXd* fvar)
 {
     int ret = _function(*t, y->data(), yvar?yvar->data():nullptr,
                             f ? f->data() : _ydiff.data(),
                             fvar?fvar->data():nullptr, _params);
     //if(f != NULL) std::memcpy(f, _ydiff, _dimension*sizeof(*f));
     return ret;
 }
 
 int GenericEulerSolver::doStep(double t, double stepSize, VectorXd* y, VectorXd* yvar)
 {
     _localError = 0;
     _localErrorRatio = 0;
 
     //int ret = _function(t, y, _ydiff, _params);
     //if(ret != OK) return ret;
 
     VectorXd* ytempvar = yvar ? &_ytempvar : nullptr;
     VectorXd* ydiffvar = yvar ? &_ydiffvar : nullptr;
 
     // Error estimation: integration with timestep = stepSize
     _yerr = - *y - stepSize*_ydiff;
     // First integration with timestep = stepSize/2
     _ytemp = *y + (stepSize/2)*_ydiff;
         
     if(yvar) { // error calculation
         *ytempvar = (yvar->array().sqrt().matrix()+(stepSize/2)*(*ydiffvar)).array().square().matrix();
     }
 
     int ret = _function(t + stepSize/2, _ytemp.data(), ytempvar?ytempvar->data():nullptr,
                         _ydiff.data(), ydiffvar?ydiffvar->data():nullptr, _params);
     if(ret != OK) return ret;
 
     for(int i=0; i<_dimension; ++i) {
         // Second integration with timestep = stepSize/2
         _ytemp[i] += stepSize/2*_ydiff[i];
         // Error estimation and solution improve
         _yerr[i] += _ytemp[i];
         // Solution improvement
         _ytemp[i] += _yerr[i];
         // Maximal error calculation
         double error = fabs(_yerr[i]);
         if(error > _localError) _localError = error;
         // Maximal error ration calculation
         double errorRatio = error / (_toleranceAbs + _toleranceRel * fabs(_ytemp[i]));
         if(errorRatio > _localErrorRatio) _localErrorRatio = errorRatio;
     }
 
     if(_localErrorRatio > 1.1) return ToleranceError;
 
     // XXX
     ret = _function(t + stepSize, _ytemp.data(), ytempvar?ytempvar->data():nullptr,
                     _ydiff.data(), ydiffvar?ydiffvar->data():nullptr, _params);
     if(ret != OK) return ret;
 
     *y = _ytemp;
 
     if(yvar) { // error calculation
         // XXX: Strictly speaking yerr are correlated between steps.
         // For now we are using the following formula which
         // assumes that yerr are equal and correlated on adjacent steps
         // TODO: improve this formula
         *yvar = (ytempvar->array().sqrt().matrix()+(stepSize/2)*(*ydiffvar)).array().square().matrix()
               + 3*_yerr.array().square().matrix();
     }
 
     return OK;
 }
 
 int GenericEulerSolver::doEvolve(double* t, double t1, VectorXd* y, VectorXd* yvar)
 {
     // XXX: add better checks
     // XXX: replace asserts by error codes here
     //      or by check in world
     // XXX: do the same checks in doStep
     STEPCORE_ASSERT_NOABORT(*t + _stepSize != *t);
     STEPCORE_ASSERT_NOABORT(*t != t1);
 
     #ifndef NDEBUG
     double realStepSize = fmin( _stepSize, t1 - *t );
     #endif
 
     const double S = 0.9;
     int result;
 
     VectorXd* ydiffvar = yvar ? &_ydiffvar : nullptr;
 
     result = _function(*t, y->data(), yvar?yvar->data():nullptr, _ydiff.data(), ydiffvar?ydiffvar->data():nullptr, _params);
     if(result != OK) return result;
 
     while(*t < t1) {
         STEPCORE_ASSERT_NOABORT(t1-*t > realStepSize/1000);
 
         //double t11 = _stepSize < t1-*t ? *t + _stepSize : t1;
         double t11 = t1 - *t > _stepSize*1.01 ? *t + _stepSize : t1;
         result = doStep(*t, t11 - *t, y, yvar);
 
         if(result != OK && result != ToleranceError) return result;
 
         if(_adaptive) {
             if(_localErrorRatio > 1.1) {
                 double r = S / _localErrorRatio;
                 if(r<0.2) r = 0.2;
                 _stepSize *= r;
             } else if(_localErrorRatio < 0.5) {
                 double r = S / pow(_localErrorRatio, 0.5);
                 if(r>5.0) r = 5.0;
                 if(r<1.0) r = 1.0;
                 double newStepSize = _stepSize*r;
                 if(newStepSize < t1 - t11) _stepSize = newStepSize;
             }
             if(result != OK) {
                 result = _function(*t, y->data(), yvar?yvar->data():nullptr, _ydiff.data(),
                                    ydiffvar?ydiffvar->data():nullptr, _params);
                 if(result != OK) return result;
                 continue;
             }
         } else {
             if(result != OK) return ToleranceError;
         }
 
         *t = t11;
     }
     return OK;
 }
 
 } // namespace StepCore