linear/pdesolver.hh

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#ifndef DUMUX_LINEAR_PDE_SOLVER_HH
#define DUMUX_LINEAR_PDE_SOLVER_HH
 
#include <cmath>
#include <memory>
#include <iostream>
#include <type_traits>
 
#include <dune/common/timer.hh>
#include <dune/common/exceptions.hh>
#include <dune/common/parallel/mpicommunication.hh>
#include <dune/common/parallel/mpihelper.hh>
#include <dune/common/std/type_traits.hh>
#include <dune/istl/bvector.hh>
#include <dune/istl/multitypeblockvector.hh>
 
#include <dumux/common/parameters.hh>
#include <dumux/common/exceptions.hh>
#include <dumux/common/typetraits/vector.hh>
#include <dumux/common/timeloop.hh>
#include <dumux/common/pdesolver.hh>
#include <dumux/linear/linearsolveracceptsmultitypematrix.hh>
#include <dumux/linear/matrixconverter.hh>
 
namespace Dumux {
 
template <class Assembler, class LinearSolver>
class LinearPDESolver : public PDESolver<Assembler, LinearSolver>
{
    using ParentType = PDESolver<Assembler, LinearSolver>;
    using Scalar = typename Assembler::Scalar;
    using JacobianMatrix = typename Assembler::JacobianMatrix;
    using SolutionVector = typename Assembler::ResidualType;
    using TimeLoop = TimeLoopBase<Scalar>;
 
public:
    LinearPDESolver(std::shared_ptr<Assembler> assembler,
                    std::shared_ptr<LinearSolver> linearSolver,
                    const std::string& paramGroup = "")
    : ParentType(assembler, linearSolver)
    , paramGroup_(paramGroup)
    , reuseMatrix_(false)
    {
        initParams_(paramGroup);
 
        // set the linear system (matrix & residual) in the assembler
        this->assembler().setLinearSystem();
    }
 
    void solve(SolutionVector& uCurrentIter) override
    {
        Dune::Timer assembleTimer(false);
        Dune::Timer solveTimer(false);
        Dune::Timer updateTimer(false);
 
        if (verbose_) {
            std::cout << "Assemble: r(x^k) = dS/dt + div F - q;   M = grad r"
                      << std::flush;
        }
 
        // assemble
 
        // linearize the problem at the current solution
        assembleTimer.start();
        if (reuseMatrix_)
            this->assembler().assembleResidual(uCurrentIter);
        else
            this->assembler().assembleJacobianAndResidual(uCurrentIter);
        assembleTimer.stop();
 
        // linear solve
 
        // Clear the current line using an ansi escape
        // sequence.  for an explanation see
        // http://en.wikipedia.org/wiki/ANSI_escape_code
        const char clearRemainingLine[] = { 0x1b, '[', 'K', 0 };
 
        if (verbose_) {
            std::cout << "\rSolve: M deltax^k = r";
            std::cout << clearRemainingLine
                      << std::flush;
        }
 
        // solve the resulting linear equation system
        solveTimer.start();
 
        // set the delta vector to zero before solving the linear system!
        SolutionVector deltaU(uCurrentIter);
        deltaU = 0;
 
        // solve by calling the appropriate implementation depending on whether the linear solver
        // is capable of handling MultiType matrices or not
        bool converged = solveLinearSystem_(deltaU);
        solveTimer.stop();
 
        if (!converged)
            DUNE_THROW(NumericalProblem, "Linear solver didn't converge.\n");
 
        // update
        if (verbose_) {
            std::cout << "\rUpdate: x^(k+1) = x^k - deltax^k";
            std::cout << clearRemainingLine;
            std::cout.flush();
        }
 
        // update the current solution and secondary variables
        updateTimer.start();
        uCurrentIter -= deltaU;
        this->assembler().updateGridVariables(uCurrentIter);
        updateTimer.stop();
 
        if (verbose_) {
            const auto elapsedTot = assembleTimer.elapsed() + solveTimer.elapsed() + updateTimer.elapsed();
            std::cout << "Assemble/solve/update time: "
                      <<  assembleTimer.elapsed() << "(" << 100*assembleTimer.elapsed()/elapsedTot << "%)/"
                      <<  solveTimer.elapsed() << "(" << 100*solveTimer.elapsed()/elapsedTot << "%)/"
                      <<  updateTimer.elapsed() << "(" << 100*updateTimer.elapsed()/elapsedTot << "%)"
                      << "\n";
        }
    }
 
    void report(std::ostream& sout = std::cout) const
    {}
 
    Scalar suggestTimeStepSize(Scalar oldTimeStep) const
    {
        return oldTimeStep;
    }
 
    void setVerbose(bool val)
    { verbose_ = val; }
 
    bool verbose() const
    { return verbose_ ; }
 
    const std::string& paramGroup() const
    { return paramGroup_; }
 
    void reuseMatrix(bool reuse = true)
    { reuseMatrix_ = reuse; }
 
private:
 
    virtual bool solveLinearSystem_(SolutionVector& deltaU)
    {
        return solveLinearSystemImpl_(this->linearSolver(),
                                      this->assembler().jacobian(),
                                      deltaU,
                                      this->assembler().residual());
    }
 
    template<class V = SolutionVector>
    typename std::enable_if_t<!isMultiTypeBlockVector<V>(), bool>
    solveLinearSystemImpl_(LinearSolver& ls,
                           JacobianMatrix& A,
                           SolutionVector& x,
                           SolutionVector& b)
    {
        constexpr auto blockSize = JacobianMatrix::block_type::rows;
        using BlockType = Dune::FieldVector<Scalar, blockSize>;
        Dune::BlockVector<BlockType> xTmp; xTmp.resize(b.size());
        Dune::BlockVector<BlockType> bTmp(xTmp);
        for (unsigned int i = 0; i < b.size(); ++i)
            for (unsigned int j = 0; j < blockSize; ++j)
                bTmp[i][j] = b[i][j];
 
        const int converged = ls.solve(A, xTmp, bTmp);
 
        for (unsigned int i = 0; i < x.size(); ++i)
            for (unsigned int j = 0; j < blockSize; ++j)
                x[i][j] = xTmp[i][j];
 
        return converged;
    }
 
 
    template<class LS = LinearSolver, class V = SolutionVector>
    typename std::enable_if_t<linearSolverAcceptsMultiTypeMatrix<LS>() &&
                              isMultiTypeBlockVector<V>(), bool>
    solveLinearSystemImpl_(LinearSolver& ls,
                           JacobianMatrix& A,
                           SolutionVector& x,
                           SolutionVector& b)
    {
        assert(this->checkSizesOfSubMatrices(A) && "Sub-blocks of MultiTypeBlockMatrix have wrong sizes!");
        return ls.solve(A, x, b);
    }
 
    template<class LS = LinearSolver, class V = SolutionVector>
    typename std::enable_if_t<!linearSolverAcceptsMultiTypeMatrix<LS>() &&
                              isMultiTypeBlockVector<V>(), bool>
    solveLinearSystemImpl_(LinearSolver& ls,
                           JacobianMatrix& A,
                           SolutionVector& x,
                           SolutionVector& b)
    {
        assert(this->checkSizesOfSubMatrices(A) && "Sub-blocks of MultiTypeBlockMatrix have wrong sizes!");
 
        // create the bcrs matrix the IterativeSolver backend can handle
        const auto M = MatrixConverter<JacobianMatrix>::multiTypeToBCRSMatrix(A);
 
        // get the new matrix sizes
        const std::size_t numRows = M.N();
        assert(numRows == M.M());
 
        // create the vector the IterativeSolver backend can handle
        const auto bTmp = VectorConverter<SolutionVector>::multiTypeToBlockVector(b);
        assert(bTmp.size() == numRows);
 
        // create a blockvector to which the linear solver writes the solution
        using VectorBlock = typename Dune::FieldVector<Scalar, 1>;
        using BlockVector = typename Dune::BlockVector<VectorBlock>;
        BlockVector y(numRows);
 
        // solve
        const bool converged = ls.solve(M, y, bTmp);
 
        // copy back the result y into x
        if(converged)
            VectorConverter<SolutionVector>::retrieveValues(x, y);
 
        return converged;
    }
 
    void initParams_(const std::string& group = "")
    {
        verbose_ = (Dune::MPIHelper::getCommunication().rank() == 0);
    }
 
    bool verbose_;
 
    std::string paramGroup_;
 
    bool reuseMatrix_;
};
 
} // end namespace Dumux
 
#endif