newtonmethod.hh

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// -*- mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
// vi: set et ts=4 sw=4 sts=4:
/*
  This file is part of the Open Porous Media project (OPM).
 
  OPM is free software: you can redistribute it and/or modify
  it under the terms of the GNU General Public License as published by
  the Free Software Foundation, either version 2 of the License, or
  (at your option) any later version.
 
  OPM is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  GNU General Public License for more details.
 
  You should have received a copy of the GNU General Public License
  along with OPM.  If not, see <http://www.gnu.org/licenses/>.
 
  Consult the COPYING file in the top-level source directory of this
  module for the precise wording of the license and the list of
  copyright holders.
*/
#ifndef EWOMS_NEWTON_METHOD_HH
#define EWOMS_NEWTON_METHOD_HH
 
#include <dune/common/classname.hh>
#include <dune/common/parallel/mpihelper.hh>
 
#include <dune/istl/istlexception.hh>
 
#include <opm/common/Exceptions.hpp>
 
#include <opm/material/densead/Math.hpp>
 
#include <opm/models/discretization/common/fvbaseproperties.hh>
 
#include <opm/models/nonlinear/newtonmethodparams.hpp>
#include <opm/models/nonlinear/newtonmethodproperties.hh>
#include <opm/models/nonlinear/nullconvergencewriter.hh>
 
#include <opm/models/utils/timer.hpp>
#include <opm/models/utils/timerguard.hh>
 
#include <opm/simulators/linalg/linalgproperties.hh>
 
#include <algorithm>
#include <cmath>
#include <cstddef>
#include <exception>
#include <iostream>
#include <sstream>
#include <string>
 
#include <unistd.h>
 
namespace Opm {
 
// forward declaration of classes
template <class TypeTag>
class NewtonMethod;
 
}
 
namespace Opm::Properties {
 
namespace TTag {
 
struct NewtonMethod {};
 
} // namespace TTag
 
// set default values for the properties
template<class TypeTag>
struct NewtonMethod<TypeTag, TTag::NewtonMethod>
{ using type = ::Opm::NewtonMethod<TypeTag>; };
 
template<class TypeTag>
struct NewtonConvergenceWriter<TypeTag, TTag::NewtonMethod>
{ using type = NullConvergenceWriter<TypeTag>; };
 
} // namespace Opm::Properties
 
namespace Opm {
 
template <class TypeTag>
class NewtonMethod
{
    using Implementation = GetPropType<TypeTag, Properties::NewtonMethod>;
    using Scalar = GetPropType<TypeTag, Properties::Scalar>;
    using Simulator = GetPropType<TypeTag, Properties::Simulator>;
    using Problem = GetPropType<TypeTag, Properties::Problem>;
    using Model = GetPropType<TypeTag, Properties::Model>;
 
    using SolutionVector = GetPropType<TypeTag, Properties::SolutionVector>;
    using GlobalEqVector = GetPropType<TypeTag, Properties::GlobalEqVector>;
    using PrimaryVariables = GetPropType<TypeTag, Properties::PrimaryVariables>;
    using Constraints = GetPropType<TypeTag, Properties::Constraints>;
    using EqVector = GetPropType<TypeTag, Properties::EqVector>;
    using Linearizer = GetPropType<TypeTag, Properties::Linearizer>;
    using LinearSolverBackend = GetPropType<TypeTag, Properties::LinearSolverBackend>;
    using ConvergenceWriter = GetPropType<TypeTag, Properties::NewtonConvergenceWriter>;
 
    using Communicator = typename Dune::MPIHelper::MPICommunicator;
    using CollectiveCommunication = typename Dune::Communication<typename Dune::MPIHelper::MPICommunicator>;
 
public:
    explicit NewtonMethod(Simulator& simulator)
        : simulator_(simulator)
        , endIterMsgStream_(std::ostringstream::out)
        , error_(1e100)
        , lastError_(1e100)
        , linearSolver_(simulator)
        , comm_(Dune::MPIHelper::getCommunicator())
        , convergenceWriter_(asImp_())
    {
        params_.read();
    }
 
    static void registerParameters()
    {
        LinearSolverBackend::registerParameters();
        NewtonMethodParams<Scalar>::registerParameters();
    }
 
    void finishInit()
    {}
 
    bool converged() const
    { return error_ <= tolerance(); }
 
    Problem& problem()
    { return simulator_.problem(); }
 
    const Problem& problem() const
    { return simulator_.problem(); }
 
    Model& model()
    { return simulator_.model(); }
 
    const Model& model() const
    { return simulator_.model(); }
 
    Scalar tolerance() const
    { return params_.tolerance_; }
 
    void setTolerance(Scalar value)
    { params_.tolerance_ = value; }
 
    void applyUpdate(SolutionVector& nextSolution,
                     const SolutionVector& currentSolution,
                     const GlobalEqVector& solutionUpdate,
                     const GlobalEqVector& currentResidual)
    {
        asImp_().update_(nextSolution, currentSolution, solutionUpdate, currentResidual);
    }
 
    bool apply()
    {
        const bool istty = isatty(fileno(stdout));
 
        // Clear the current line using an ansi escape
        // sequence.  For an explanation see
        // http://en.wikipedia.org/wiki/ANSI_escape_code
        const char* clearRemainingLine = "\n";
        if (istty) {
            static const char ansiClear[] = { 0x1b, '[', 'K', '\r', 0 };
            clearRemainingLine = ansiClear;
        }
 
        // make sure all timers are prestine
        prePostProcessTimer_.halt();
        linearizeTimer_.halt();
        solveTimer_.halt();
        updateTimer_.halt();
 
        SolutionVector& nextSolution = model().solution(/*historyIdx=*/0);
        SolutionVector currentSolution(nextSolution);
        GlobalEqVector solutionUpdate(nextSolution.size());
 
        Linearizer& linearizer = model().linearizer();
 
        TimerGuard prePostProcessTimerGuard(prePostProcessTimer_);
 
        // tell the implementation that we begin solving
        prePostProcessTimer_.start();
        asImp_().begin_(nextSolution);
        prePostProcessTimer_.stop();
 
        try {
            TimerGuard innerPrePostProcessTimerGuard(prePostProcessTimer_);
            TimerGuard linearizeTimerGuard(linearizeTimer_);
            TimerGuard updateTimerGuard(updateTimer_);
            TimerGuard solveTimerGuard(solveTimer_);
 
            // execute the method as long as the implementation thinks
            // that we should do another iteration
            while (asImp_().proceed_()) {
                // linearize the problem at the current solution
 
                // notify the implementation that we're about to start
                // a new iteration
                prePostProcessTimer_.start();
                asImp_().beginIteration_();
                prePostProcessTimer_.stop();
 
                // make the current solution to the old one
                currentSolution = nextSolution;
 
                if (asImp_().verbose_()) {
                    std::cout << "Linearize: r(x^k) = dS/dt + div F - q;   M = grad r"
                              << clearRemainingLine
                              << std::flush;
                }
 
                // do the actual linearization
                linearizeTimer_.start();
                asImp_().linearizeDomain_();
                asImp_().linearizeAuxiliaryEquations_();
                linearizeTimer_.stop();
 
                solveTimer_.start();
                auto& residual = linearizer.residual();
                const auto& jacobian = linearizer.jacobian();
                linearSolver_.prepare(jacobian, residual);
                linearSolver_.setResidual(residual);
                linearSolver_.getResidual(residual);
                solveTimer_.stop();
 
                // The preSolve_() method usually computes the errors, but it can do
                // something else in addition. TODO: should its costs be counted to
                // the linearization or to the update?
                updateTimer_.start();
                asImp_().preSolve_(currentSolution, residual);
                updateTimer_.stop();
 
                if (!asImp_().proceed_()) {
                    if (asImp_().verbose_() && istty) {
                        std::cout << clearRemainingLine << std::flush;
                    }
 
                    // tell the implementation that we're done with this iteration
                    prePostProcessTimer_.start();
                    asImp_().endIteration_(nextSolution, currentSolution);
                    prePostProcessTimer_.stop();
 
                    break;
                }
 
                // solve the resulting linear equation system
                if (asImp_().verbose_()) {
                    std::cout << "Solve: M deltax^k = r"
                              << clearRemainingLine
                              << std::flush;
                }
 
                solveTimer_.start();
                // solve A x = b, where b is the residual, A is its Jacobian and x is the
                // update of the solution
                linearSolver_.setMatrix(jacobian);
                solutionUpdate = 0.0;
                const bool conv = linearSolver_.solve(solutionUpdate);
                solveTimer_.stop();
 
                if (!conv) {
                    solveTimer_.stop();
                    if (asImp_().verbose_()) {
                        std::cout << "Newton: Linear solver did not converge\n" << std::flush;
                    }
 
                    prePostProcessTimer_.start();
                    asImp_().failed_();
                    prePostProcessTimer_.stop();
 
                    return false;
                }
 
                // update the solution
                if (asImp_().verbose_()) {
                    std::cout << "Update: x^(k+1) = x^k - deltax^k"
                              << clearRemainingLine
                              << std::flush;
                }
 
                // update the current solution (i.e. uOld) with the delta
                // (i.e. u). The result is stored in u
                updateTimer_.start();
                asImp_().postSolve_(currentSolution,
                                    residual,
                                    solutionUpdate);
                asImp_().update_(nextSolution, currentSolution, solutionUpdate, residual);
                updateTimer_.stop();
 
                if (asImp_().verbose_() && istty) {
                    // make sure that the line currently holding the cursor is prestine
                    std::cout << clearRemainingLine
                              << std::flush;
                }
 
                // tell the implementation that we're done with this iteration
                prePostProcessTimer_.start();
                asImp_().endIteration_(nextSolution, currentSolution);
                prePostProcessTimer_.stop();
            }
        }
        catch (const Dune::Exception& e)
        {
            if (asImp_().verbose_()) {
                std::cout << "Newton method caught exception: \""
                          << e.what() << "\"\n" << std::flush;
            }
 
            prePostProcessTimer_.start();
            asImp_().failed_();
            prePostProcessTimer_.stop();
 
            return false;
        }
        catch (const NumericalProblem& e)
        {
            if (asImp_().verbose_()) {
                std::cout << "Newton method caught exception: \""
                          << e.what() << "\"\n" << std::flush;
            }
 
            prePostProcessTimer_.start();
            asImp_().failed_();
            prePostProcessTimer_.stop();
 
            return false;
        }
 
        // clear current line on terminal
        if (asImp_().verbose_() && istty) {
            std::cout << clearRemainingLine
                      << std::flush;
        }
 
        // tell the implementation that we're done
        prePostProcessTimer_.start();
        asImp_().end_();
        prePostProcessTimer_.stop();
 
        // print the timing summary of the time step
        if (asImp_().verbose_()) {
            Scalar elapsedTot =
                linearizeTimer_.realTimeElapsed() +
                solveTimer_.realTimeElapsed() +
                updateTimer_.realTimeElapsed();
            std::cout << "Linearization/solve/update time: "
                      << linearizeTimer_.realTimeElapsed() << "("
                      << 100 * linearizeTimer_.realTimeElapsed() / elapsedTot << "%)/"
                      << solveTimer_.realTimeElapsed() << "("
                      << 100 * solveTimer_.realTimeElapsed() / elapsedTot << "%)/"
                      << updateTimer_.realTimeElapsed() << "("
                      << 100 * updateTimer_.realTimeElapsed() / elapsedTot << "%)"
                      << "\n" << std::flush;
        }
 
 
        // if we're not converged, tell the implementation that we've failed
        if (!asImp_().converged()) {
            prePostProcessTimer_.start();
            asImp_().failed_();
            prePostProcessTimer_.stop();
            return false;
        }
 
        // if we converged, tell the implementation that we've succeeded
        prePostProcessTimer_.start();
        asImp_().succeeded_();
        prePostProcessTimer_.stop();
 
        return true;
    }
 
    Scalar suggestTimeStepSize(Scalar oldDt) const
    {
        // be aggressive reducing the time-step size but
        // conservative when increasing it. the rationale is
        // that we want to avoid failing in the next time
        // integration which would be quite expensive
        const int nit = problem().iterationContext().iteration();
        if (lastSolveFailed_ || nit > params_.targetIterations_) {
            const int overshoot = lastSolveFailed_ ? params_.targetIterations_ : nit - params_.targetIterations_;
            Scalar percent = Scalar(overshoot) / params_.targetIterations_;
            Scalar nextDt = std::max(problem().minTimeStepSize(),
                                     oldDt / (Scalar{1.0} + percent));
            return nextDt;
        }
 
        Scalar percent = Scalar(params_.targetIterations_ - nit) / params_.targetIterations_;
        Scalar nextDt = std::max(problem().minTimeStepSize(),
                                 oldDt*(Scalar{1.0} + percent / Scalar{1.2}));
        return nextDt;
    }
 
    std::ostringstream& endIterMsg()
    { return endIterMsgStream_; }
 
    void eraseMatrix()
    { linearSolver_.eraseMatrix(); }
 
    LinearSolverBackend& linearSolver()
    { return linearSolver_; }
 
    const LinearSolverBackend& linearSolver() const
    { return linearSolver_; }
 
    const Timer& prePostProcessTimer() const
    { return prePostProcessTimer_; }
 
    const Timer& linearizeTimer() const
    { return linearizeTimer_; }
 
    const Timer& solveTimer() const
    { return solveTimer_; }
 
    const Timer& updateTimer() const
    { return updateTimer_; }
 
protected:
    bool verbose_() const
    { return params_.verbose_ && (comm_.rank() == 0); }
 
    void begin_(const SolutionVector&)
    {
        lastSolveFailed_ = false;
 
        problem().resetIterationForNewTimestep();
 
        if (params_.writeConvergence_) {
            convergenceWriter_.beginTimeStep();
        }
    }
 
    void beginIteration_()
    {
        // start with a clean message stream
        endIterMsgStream_.str("");
        const auto& comm = simulator_.gridView().comm();
        bool succeeded = true;
        try {
            problem().beginIteration();
        }
        catch (const std::exception& e) {
            succeeded = false;
 
            std::cout << "rank " << simulator_.gridView().comm().rank()
                      << " caught an exception while pre-processing the problem:" << e.what()
                      << "\n"  << std::flush;
        }
 
        succeeded = comm.min(succeeded);
 
        if (!succeeded) {
            throw NumericalProblem("pre processing of the problem failed");
        }
 
        lastError_ = error_;
    }
 
    void linearizeDomain_()
    { model().linearizer().linearizeDomain(); }
 
    void linearizeAuxiliaryEquations_()
    {
        model().linearizer().linearizeAuxiliaryEquations();
        model().linearizer().finalize();
    }
 
    void preSolve_(const SolutionVector&,
                   const GlobalEqVector& currentResidual)
    {
        const auto& constraintsMap = model().linearizer().constraintsMap();
        lastError_ = error_;
        Scalar newtonMaxError = params_.maxError_;
 
        // calculate the error as the maximum weighted tolerance of
        // the solution's residual
        error_ = 0;
        for (unsigned dofIdx = 0; dofIdx < currentResidual.size(); ++dofIdx) {
            // do not consider auxiliary DOFs for the error
            if (dofIdx >= model().numGridDof() || model().dofTotalVolume(dofIdx) <= 0.0) {
                continue;
            }
 
            // also do not consider DOFs which are constraint
            if (enableConstraints_()) {
                if (constraintsMap.count(dofIdx) > 0) {
                    continue;
                }
            }
 
            const auto& r = currentResidual[dofIdx];
            for (unsigned eqIdx = 0; eqIdx < r.size(); ++eqIdx) {
                error_ = max(std::abs(r[eqIdx] * model().eqWeight(dofIdx, eqIdx)), error_);
            }
        }
 
        // take the other processes into account
        error_ = comm_.max(error_);
 
        // make sure that the error never grows beyond the maximum
        // allowed one
        if (error_ > newtonMaxError) {
            throw NumericalProblem("Newton: Error " + std::to_string(double(error_)) +
                                   " is larger than maximum allowed error of " +
                                   std::to_string(double(newtonMaxError)));
        }
    }
 
    void postSolve_(const SolutionVector&,
                    const GlobalEqVector&,
                    GlobalEqVector& solutionUpdate)
    {
        // loop over the auxiliary modules and ask them to post process the solution
        // vector.
        const auto& comm = simulator_.gridView().comm();
        for (unsigned i = 0; i < simulator_.model().numAuxiliaryModules(); ++i) {
            bool succeeded = true;
            try {
                simulator_.model().auxiliaryModule(i)->postSolve(solutionUpdate);
            }
            catch (const std::exception& e) {
                succeeded = false;
 
                std::cout << "rank " << simulator_.gridView().comm().rank()
                          << " caught an exception while post processing an auxiliary module:" << e.what()
                          << "\n"  << std::flush;
            }
 
            succeeded = comm.min(succeeded);
 
            if (!succeeded) {
                throw NumericalProblem("post processing of an auxilary equation failed");
            }
        }
    }
 
    void update_(SolutionVector& nextSolution,
                 const SolutionVector& currentSolution,
                 const GlobalEqVector& solutionUpdate,
                 const GlobalEqVector& currentResidual)
    {
        const auto& constraintsMap = model().linearizer().constraintsMap();
 
        // first, write out the current solution to make convergence
        // analysis possible
        asImp_().writeConvergence_(currentSolution, solutionUpdate);
 
        // make sure not to swallow non-finite values at this point
        if (!std::isfinite(solutionUpdate.one_norm())) {
            throw NumericalProblem("Non-finite update!");
        }
 
        std::size_t numGridDof = model().numGridDof();
        for (unsigned dofIdx = 0; dofIdx < numGridDof; ++dofIdx) {
            if (enableConstraints_()) {
                if (constraintsMap.count(dofIdx) > 0) {
                    const auto& constraints = constraintsMap.at(dofIdx);
                    asImp_().updateConstraintDof_(dofIdx,
                                                  nextSolution[dofIdx],
                                                  constraints);
                }
                else {
                    asImp_().updatePrimaryVariables_(dofIdx,
                                                     nextSolution[dofIdx],
                                                     currentSolution[dofIdx],
                                                     solutionUpdate[dofIdx],
                                                     currentResidual[dofIdx]);
                }
            }
            else {
                asImp_().updatePrimaryVariables_(dofIdx,
                                                 nextSolution[dofIdx],
                                                 currentSolution[dofIdx],
                                                 solutionUpdate[dofIdx],
                                                 currentResidual[dofIdx]);
            }
        }
 
        // update the DOFs of the auxiliary equations
        std::size_t numDof = model().numTotalDof();
        for (std::size_t dofIdx = numGridDof; dofIdx < numDof; ++dofIdx) {
            nextSolution[dofIdx] = currentSolution[dofIdx];
            nextSolution[dofIdx] -= solutionUpdate[dofIdx];
        }
    }
 
    void updateConstraintDof_(unsigned,
                              PrimaryVariables& nextValue,
                              const Constraints& constraints)
    { nextValue = constraints; }
 
    void updatePrimaryVariables_(unsigned,
                                 PrimaryVariables& nextValue,
                                 const PrimaryVariables& currentValue,
                                 const EqVector& update,
                                 const EqVector&)
    {
        nextValue = currentValue;
        nextValue -= update;
    }
 
    void writeConvergence_(const SolutionVector& currentSolution,
                           const GlobalEqVector& solutionUpdate)
    {
        if (params_.writeConvergence_) {
            convergenceWriter_.beginIteration();
            convergenceWriter_.writeFields(currentSolution, solutionUpdate);
            convergenceWriter_.endIteration();
        }
    }
 
    void endIteration_(const SolutionVector&,
                       const SolutionVector&)
    {
        problem().advanceIteration();
 
        const auto& comm = simulator_.gridView().comm();
        bool succeeded = true;
        try {
            problem().endIteration();
        }
        catch (const std::exception& e) {
            succeeded = false;
 
            std::cout << "rank " << simulator_.gridView().comm().rank()
                      << " caught an exception while letting the problem post-process:" << e.what()
                      << "\n"  << std::flush;
        }
 
        succeeded = comm.min(succeeded);
 
        if (!succeeded) {
            throw NumericalProblem("post processing of the problem failed");
        }
 
        if (asImp_().verbose_()) {
            std::cout << "Newton iteration " << problem().iterationContext().iteration() << ""
                      << " error: " << error_
                      << endIterMsg().str() << "\n" << std::flush;
        }
    }
 
    bool proceed_() const
    {
        if (problem().iterationContext().iteration() < 1) {
            return true; // we always do at least one full iteration
        }
        else if (asImp_().converged()) {
            // we are below the specified tolerance, so we don't have to
            // do more iterations
            return false;
        }
        else if (problem().iterationContext().iteration() >= params_.maxIterations_) {
            // we have exceeded the allowed number of steps.  If the
            // error was reduced by a factor of at least 4,
            // in the last iterations we proceed even if we are above
            // the maximum number of steps
            return error_ * 4.0 < lastError_;
        }
 
        return true;
    }
 
    void end_()
    {
        if (params_.writeConvergence_) {
            convergenceWriter_.endTimeStep();
        }
    }
 
    void failed_()
    { lastSolveFailed_ = true; }
 
    void succeeded_()
    {}
 
    static bool enableConstraints_()
    { return getPropValue<TypeTag, Properties::EnableConstraints>(); }
 
    Simulator& simulator_;
 
    Timer prePostProcessTimer_;
    Timer linearizeTimer_;
    Timer solveTimer_;
    Timer updateTimer_;
 
    std::ostringstream endIterMsgStream_;
 
    Scalar error_;
    Scalar lastError_;
    NewtonMethodParams<Scalar> params_;
 
    // Flags that the last solve failed, triggering a timestep reduction.
    bool lastSolveFailed_ = false;
    // the linear solver
    LinearSolverBackend linearSolver_;
 
    // the collective communication used by the simulation (i.e. fake
    // or MPI)
    CollectiveCommunication comm_;
 
    // the object which writes the convergence behaviour of the Newton
    // method to disk
    ConvergenceWriter convergenceWriter_;
 
private:
    Implementation& asImp_()
    { return *static_cast<Implementation *>(this); }
 
    const Implementation& asImp_() const
    { return *static_cast<const Implementation *>(this); }
};
 
} // namespace Opm
 
#endif