NonlinearSolver.cpp

Go to the documentation of this file.

// SPDX-FileCopyrightText: Copyright (c) OpenGeoSys Community (opengeosys.org)
// SPDX-License-Identifier: BSD-3-Clause
 
#include "NonlinearSolver.h"
 
#include <algorithm>
#include <boost/algorithm/string.hpp>
 
#include "BaseLib/ConfigTree.h"
#include "BaseLib/Error.h"
#include "BaseLib/Logging.h"
#include "BaseLib/MPI.h"
#include "BaseLib/RunTime.h"
#include "ConvergenceCriterion.h"
#include "MathLib/LinAlg/LinAlg.h"
#include "NumLib/DOF/GlobalMatrixProviders.h"
#include "NumLib/Exceptions.h"
#include "PETScNonlinearSolver.h"
 
namespace NumLib
{
namespace detail
{
#if !defined(USE_PETSC) && !defined(USE_LIS)
bool solvePicard(GlobalLinearSolver& linear_solver, GlobalMatrix& A,
                 GlobalVector& rhs, GlobalVector& x,
                 MathLib::LinearSolverBehaviour const linear_solver_behaviour)
{
    BaseLib::RunTime time_linear_solver;
    time_linear_solver.start();
 
    if (!linear_solver.compute(A, linear_solver_behaviour))
    {
        ERR("Picard: The linear solver failed in the compute() step.");
        return false;
    }
 
    bool const iteration_succeeded = linear_solver.solve(rhs, x);
 
    INFO("[time] Linear solver took {:g} s.", time_linear_solver.elapsed());
 
    if (iteration_succeeded)
    {
        return true;
    }
 
    ERR("Picard: The linear solver failed in the solve() step.");
    return false;
}
#else
bool solvePicard(GlobalLinearSolver& linear_solver, GlobalMatrix& A,
                 GlobalVector& rhs, GlobalVector& x,
                 MathLib::LinearSolverBehaviour const linear_solver_behaviour)
{
    if (linear_solver_behaviour ==
            MathLib::LinearSolverBehaviour::RECOMPUTE_AND_STORE ||
        linear_solver_behaviour == MathLib::LinearSolverBehaviour::REUSE)
    {
        WARN(
            "The performance optimization to skip the linear solver compute() "
            "step is not implemented for PETSc or LIS linear solvers.");
    }
 
    BaseLib::RunTime time_linear_solver;
    time_linear_solver.start();
 
    bool const iteration_succeeded = linear_solver.solve(A, rhs, x);
 
    INFO("[time] Linear solver took {:g} s.", time_linear_solver.elapsed());
 
    if (iteration_succeeded)
    {
        return true;
    }
 
    ERR("Picard: The linear solver failed in the solve() step.");
    return false;
}
#endif
}  // namespace detail
 
void NonlinearSolver<NonlinearSolverTag::Picard>::
    calculateNonEquilibriumInitialResiduum(
        std::vector<GlobalVector*> const& x,
        std::vector<GlobalVector*> const& x_prev, int const process_id)
{
    if (!_compensate_non_equilibrium_initial_residuum)
    {
        return;
    }
 
    INFO("Calculate non-equilibrium initial residuum.");
 
    auto& A = NumLib::GlobalMatrixProvider::provider.getMatrix(_A_id);
    auto& rhs = NumLib::GlobalVectorProvider::provider.getVector(_rhs_id);
    _equation_system->assemble(x, x_prev, process_id);
    _equation_system->getA(A);
    _equation_system->getRhs(*x_prev[process_id], rhs);
 
    // r_neq = A * x - rhs
    _r_neq = &NumLib::GlobalVectorProvider::provider.getVector(_r_neq_id);
    MathLib::LinAlg::matMult(A, *x[process_id], *_r_neq);
    MathLib::LinAlg::axpy(*_r_neq, -1.0, rhs);  // res -= rhs
 
    // Set the values of the selected entries of _r_neq, which are associated
    // with the equations that do not need initial residual compensation, to
    // zero.
    const auto selected_global_indices =
        _equation_system->getIndicesOfResiduumWithoutInitialCompensation();
 
    std::vector<double> zero_entries(selected_global_indices.size(), 0.0);
    _r_neq->set(selected_global_indices, zero_entries);
    _equation_system->setReleaseNodalForces(_r_neq, process_id);
 
    MathLib::LinAlg::finalizeAssembly(*_r_neq);
 
    NumLib::GlobalMatrixProvider::provider.releaseMatrix(A);
    NumLib::GlobalVectorProvider::provider.releaseVector(rhs);
}
 
NonlinearSolverStatus NonlinearSolver<NonlinearSolverTag::Picard>::solve(
    std::vector<GlobalVector*>& x,
    std::vector<GlobalVector*> const& x_prev,
    std::function<void(int, bool, std::vector<GlobalVector*> const&)> const&
        postIterationCallback,
    int const process_id)
{
    namespace LinAlg = MathLib::LinAlg;
    auto& sys = *_equation_system;
 
    auto& A = NumLib::GlobalMatrixProvider::provider.getMatrix(_A_id);
    auto& rhs = NumLib::GlobalVectorProvider::provider.getVector(_rhs_id);
 
    std::vector<GlobalVector*> x_new{x};
    x_new[process_id] =
        &NumLib::GlobalVectorProvider::provider.getVector(_x_new_id);
    LinAlg::copy(*x[process_id], *x_new[process_id]);  // set initial guess
 
    bool error_norms_met = false;
 
    _convergence_criterion->preFirstIteration();
 
    int iteration = 1;
    for (; iteration <= _maxiter; ++iteration, _convergence_criterion->reset())
    {
        BaseLib::RunTime timer_dirichlet;
        double time_dirichlet = 0.0;
 
        BaseLib::RunTime time_iteration;
        time_iteration.start();
 
        INFO("Iteration #{:d} started.", iteration);
        timer_dirichlet.start();
        auto& x_new_process = *x_new[process_id];
        LinAlg::setLocalAccessibleVector(x_new_process);
        sys.computeKnownSolutions(x_new_process, process_id);
        sys.applyKnownSolutions(x_new_process);
        time_dirichlet += timer_dirichlet.elapsed();
 
        sys.preIteration(iteration, x_new_process);
 
        BaseLib::RunTime time_assembly;
        time_assembly.start();
        sys.assemble(x_new, x_prev, process_id);
        sys.getA(A);
        sys.getRhs(*x_prev[process_id], rhs);
 
        // Normalize the linear equation system, if required
        if (sys.requiresNormalization() &&
            !_linear_solver.canSolveRectangular())
        {
            sys.getAandRhsNormalized(A, rhs);
            WARN(
                "The equation system is rectangular, but the current linear "
                "solver only supports square systems. "
                "The system will be normalized, which lead to a squared "
                "condition number and potential numerical issues. "
                "It is recommended to use a solver that supports rectangular "
                "equation systems for better numerical stability.");
        }
 
        INFO("[time] Assembly took {:g} s.", time_assembly.elapsed());
 
        // Subtract non-equilibrium initial residuum if set
        if (_r_neq != nullptr)
        {
            LinAlg::axpy(rhs, -1, *_r_neq);
        }
 
        auto const solver_needs_to_compute = sys.linearSolverNeedsToCompute();
        bool const solver_will_compute =
            _linear_solver.willCompute(solver_needs_to_compute);
 
        timer_dirichlet.start();
        sys.applyKnownSolutionsPicard(
            A, rhs, x_new_process,
            solver_will_compute
                ? MathLib::DirichletBCApplicationMode::COMPLETE_MATRIX_UPDATE
                : MathLib::DirichletBCApplicationMode::
                      FAST_INCOMPLETE_MATRIX_UPDATE);
        time_dirichlet += timer_dirichlet.elapsed();
        INFO("[time] Applying Dirichlet BCs took {:g} s.", time_dirichlet);
 
        if (!sys.isLinear() && _convergence_criterion->hasResidualCheck())
        {
            if (!solver_will_compute)
            {
                // !solver_will_compute means that the Dirichlet BC application
                // is incomplete (i.e., A not properly modified) and the
                // computed residual is wrong.
                OGS_FATAL(
                    "Logic error. The solver skips the compute step for a "
                    "non-linear equation system.");
            }
            GlobalVector res;
            LinAlg::matMult(A, x_new_process, res);  // res = A * x_new
            LinAlg::axpy(res, -1.0, rhs);            // res -= rhs
            _convergence_criterion->checkResidual(res);
        }
 
        bool iteration_succeeded = detail::solvePicard(
            _linear_solver, A, rhs, x_new_process, solver_needs_to_compute);
 
        if (iteration_succeeded)
        {
            if (postIterationCallback)
            {
                postIterationCallback(iteration, error_norms_met, x_new);
            }
 
            switch (sys.postIteration(x_new_process))
            {
                case IterationResult::SUCCESS:
                    // Don't copy here. The old x might still be used further
                    // below. Although currently it is not.
                    break;
                case IterationResult::FAILURE:
                    ERR("Picard: The postIteration() hook reported a "
                        "non-recoverable error.");
                    iteration_succeeded = false;
                    // Copy new solution to x.
                    // Thereby the failed solution can be used by the caller for
                    // debugging purposes.
                    LinAlg::copy(x_new_process, *x[process_id]);
                    break;
                case IterationResult::REPEAT_ITERATION:
                    INFO(
                        "Picard: The postIteration() hook decided that this "
                        "iteration has to be repeated.");
                    LinAlg::copy(
                        *x[process_id],
                        x_new_process);  // throw the iteration result away
                    continue;
            }
        }
 
        if (!iteration_succeeded)
        {
            // Don't compute error norms, break here.
            error_norms_met = false;
            break;
        }
 
        if (sys.isLinear())
        {
            error_norms_met = true;
        }
        else
        {
            if (_convergence_criterion->hasDeltaXCheck())
            {
                GlobalVector minus_delta_x(*x[process_id]);
                LinAlg::axpy(minus_delta_x, -1.0,
                             x_new_process);  // minus_delta_x = x - x_new
                _convergence_criterion->checkDeltaX(minus_delta_x,
                                                    x_new_process);
            }
 
            error_norms_met = _convergence_criterion->isSatisfied();
        }
 
        // Update x s.t. in the next iteration we will compute the right delta x
        LinAlg::copy(x_new_process, *x[process_id]);
 
        INFO("[time] Iteration #{:d} took {:g} s.", iteration,
             time_iteration.elapsed());
 
        if (error_norms_met)
        {
            break;
        }
 
        // Avoid increment of the 'iteration' if the error norms are not met,
        // but maximum number of iterations is reached.
        if (iteration >= _maxiter)
        {
            break;
        }
    }
 
    if (iteration > _maxiter)
    {
        ERR("Picard: Could not solve the given nonlinear system within {:d} "
            "iterations",
            _maxiter);
    }
 
    NumLib::GlobalMatrixProvider::provider.releaseMatrix(A);
    NumLib::GlobalVectorProvider::provider.releaseVector(rhs);
    NumLib::GlobalVectorProvider::provider.releaseVector(*x_new[process_id]);
 
    return {error_norms_met, iteration};
}
 
void NonlinearSolver<NonlinearSolverTag::Newton>::
    calculateNonEquilibriumInitialResiduum(
        std::vector<GlobalVector*> const& x,
        std::vector<GlobalVector*> const& x_prev, int const process_id)
{
    if (!_compensate_non_equilibrium_initial_residuum)
    {
        return;
    }
 
    INFO("Calculate non-equilibrium initial residuum.");
 
    _equation_system->assemble(x, x_prev, process_id);
    _r_neq = &NumLib::GlobalVectorProvider::provider.getVector(_r_neq_id);
    _equation_system->getResidual(*x[process_id], *x_prev[process_id], *_r_neq);
 
    // Set the values of the selected entries of _r_neq, which are associated
    // with the equations that do not need initial residual compensation, to
    // zero.
    const auto selected_global_indices =
        _equation_system->getIndicesOfResiduumWithoutInitialCompensation();
    std::vector<double> zero_entries(selected_global_indices.size(), 0.0);
 
    _r_neq->set(selected_global_indices, zero_entries);
    _equation_system->setReleaseNodalForces(_r_neq, process_id);
 
    MathLib::LinAlg::finalizeAssembly(*_r_neq);
}
 
NonlinearSolverStatus NonlinearSolver<NonlinearSolverTag::Newton>::solve(
    std::vector<GlobalVector*>& x,
    std::vector<GlobalVector*> const& x_prev,
    std::function<void(int, bool, std::vector<GlobalVector*> const&)> const&
        postIterationCallback,
    int const process_id)
{
    namespace LinAlg = MathLib::LinAlg;
    auto& sys = *_equation_system;
 
    auto& res = NumLib::GlobalVectorProvider::provider.getVector(_res_id);
    auto& minus_delta_x =
        NumLib::GlobalVectorProvider::provider.getVector(_minus_delta_x_id);
    auto& J = NumLib::GlobalMatrixProvider::provider.getMatrix(_J_id);
 
    bool error_norms_met = false;
 
    // TODO be more efficient
    // init minus_delta_x to the right size
    LinAlg::copy(*x[process_id], minus_delta_x);
 
    _convergence_criterion->preFirstIteration();
 
    int iteration = 1;
#if !defined(USE_PETSC) && !defined(USE_LIS)
    int next_iteration_inv_jacobian_recompute = 1;
#endif
    for (; iteration <= _maxiter; ++iteration, _convergence_criterion->reset())
    {
        BaseLib::RunTime timer_dirichlet;
        double time_dirichlet = 0.0;
 
        BaseLib::RunTime time_iteration;
        INFO("Iteration #{:d} started.", iteration);
        time_iteration.start();
 
        timer_dirichlet.start();
        sys.computeKnownSolutions(*x[process_id], process_id);
        time_dirichlet += timer_dirichlet.elapsed();
 
        sys.preIteration(iteration, *x[process_id]);
 
        BaseLib::RunTime time_assembly;
        time_assembly.start();
        bool mpi_rank_assembly_ok = true;
        try
        {
            sys.assemble(x, x_prev, process_id);
        }
        catch (AssemblyException const& e)
        {
            ERR("Abort nonlinear iteration. Repeating timestep. Reason: {:s}",
                e.what());
            error_norms_met = false;
            iteration = _maxiter;
            mpi_rank_assembly_ok = false;
        }
        if (BaseLib::MPI::anyOf(!mpi_rank_assembly_ok))
        {
            break;
        }
        sys.getResidual(*x[process_id], *x_prev[process_id], res);
        sys.getJacobian(J);
        INFO("[time] Assembly took {:g} s.", time_assembly.elapsed());
 
        // Subtract non-equilibrium initial residuum if set
        if (_r_neq != nullptr)
            LinAlg::axpy(res, -1, *_r_neq);
 
        minus_delta_x.setZero();
 
        timer_dirichlet.start();
        sys.applyKnownSolutionsNewton(J, res, *x[process_id], minus_delta_x);
        time_dirichlet += timer_dirichlet.elapsed();
        INFO("[time] Applying Dirichlet BCs took {:g} s.", time_dirichlet);
 
        if (!sys.isLinear() && _convergence_criterion->hasResidualCheck())
        {
            _convergence_criterion->checkResidual(res);
        }
 
        BaseLib::RunTime time_linear_solver;
        time_linear_solver.start();
#if !defined(USE_PETSC) && !defined(USE_LIS)
        auto linear_solver_behaviour = MathLib::LinearSolverBehaviour::REUSE;
        if (iteration == next_iteration_inv_jacobian_recompute)
        {
            linear_solver_behaviour =
                MathLib::LinearSolverBehaviour::RECOMPUTE_AND_STORE;
            next_iteration_inv_jacobian_recompute =
                next_iteration_inv_jacobian_recompute + _recompute_jacobian;
        }
 
        bool iteration_succeeded = false;
        if (!_linear_solver.compute(J, linear_solver_behaviour))
        {
            ERR("Newton: The linear solver failed in the compute() step.");
        }
        else
        {
            iteration_succeeded = _linear_solver.solve(res, minus_delta_x);
        }
#else
        bool iteration_succeeded = _linear_solver.solve(J, res, minus_delta_x);
#endif
        INFO("[time] Linear solver took {:g} s.", time_linear_solver.elapsed());
 
        if (!iteration_succeeded)
        {
            ERR("Newton: The linear solver failed.");
        }
        else
        {
            // TODO could be solved in a better way
            // cf.
            // https://petsc.org/release/manualpages/Vec/VecWAXPY
 
            // Copy pointers, replace the one for the given process id.
            std::vector<GlobalVector*> x_new{x};
            x_new[process_id] =
                &NumLib::GlobalVectorProvider::provider.getVector(
                    *x[process_id], _x_new_id);
            double damping = _damping;
            if (_convergence_criterion->hasNonNegativeDamping())
            {
                damping = _convergence_criterion->getDampingFactor(
                    minus_delta_x, *x[process_id], _damping);
            }
            if (_damping_reduction)
            {
                damping =
                    damping +
                    (1 - damping) *
                        std::clamp(iteration / *_damping_reduction, 0.0, 1.0);
            }
            LinAlg::axpy(*x_new[process_id], -damping, minus_delta_x);
 
            if (postIterationCallback)
            {
                postIterationCallback(iteration, error_norms_met, x_new);
            }
 
            switch (sys.postIteration(*x_new[process_id]))
            {
                case IterationResult::SUCCESS:
                    break;
                case IterationResult::FAILURE:
                    ERR("Newton: The postIteration() hook reported a "
                        "non-recoverable error.");
                    iteration_succeeded = false;
                    break;
                case IterationResult::REPEAT_ITERATION:
                    INFO(
                        "Newton: The postIteration() hook decided that this "
                        "iteration has to be repeated.");
                    // TODO introduce some onDestroy hook.
                    NumLib::GlobalVectorProvider::provider.releaseVector(
                        *x_new[process_id]);
                    continue;  // That throws the iteration result away.
            }
 
            LinAlg::copy(*x_new[process_id],
                         *x[process_id]);  // copy new solution to x
            NumLib::GlobalVectorProvider::provider.releaseVector(
                *x_new[process_id]);
        }
 
        if (!iteration_succeeded)
        {
            // Don't compute further error norms, but break here.
            error_norms_met = false;
            break;
        }
 
        if (sys.isLinear())
        {
            error_norms_met = true;
        }
        else
        {
            if (_convergence_criterion->hasDeltaXCheck())
            {
                // Note: x contains the new solution!
                _convergence_criterion->checkDeltaX(minus_delta_x,
                                                    *x[process_id]);
            }
 
            error_norms_met = _convergence_criterion->isSatisfied();
        }
 
        INFO("[time] Iteration #{:d} took {:g} s.", iteration,
             time_iteration.elapsed());
 
        if (error_norms_met)
        {
            break;
        }
 
        // Avoid increment of the 'iteration' if the error norms are not met,
        // but maximum number of iterations is reached.
        if (iteration >= _maxiter)
        {
            break;
        }
    }
 
    if (iteration > _maxiter)
    {
        ERR("Newton: Could not solve the given nonlinear system within {:d} "
            "iterations",
            _maxiter);
    }
 
    NumLib::GlobalMatrixProvider::provider.releaseMatrix(J);
    NumLib::GlobalVectorProvider::provider.releaseVector(res);
    NumLib::GlobalVectorProvider::provider.releaseVector(minus_delta_x);
 
    return {error_norms_met, iteration};
}
 
std::pair<std::unique_ptr<NonlinearSolverBase>, NonlinearSolverTag>
createNonlinearSolver(GlobalLinearSolver& linear_solver,
                      BaseLib::ConfigTree const& config)
{
    auto const type = config.getConfigParameter<std::string>("type");
    auto const max_iter = config.getConfigParameter<int>("max_iter");
 
    if (type == "Picard")
    {
        auto const tag = NonlinearSolverTag::Picard;
        using ConcreteNLS = NonlinearSolver<tag>;
        return std::make_pair(
            std::make_unique<ConcreteNLS>(linear_solver, max_iter), tag);
    }
    if (type == "Newton")
    {
        auto const recompute_jacobian =
            config.getConfigParameter<int>("recompute_jacobian", 1);

        auto const damping = config.getConfigParameter<double>("damping", 1.0);
        if (damping <= 0)
        {
            OGS_FATAL(
                "The damping factor for the Newon method must be positive, got "
                "{:g}.",
                damping);
        }
        auto const damping_reduction =
            config.getConfigParameterOptional<double>("damping_reduction");
        auto const tag = NonlinearSolverTag::Newton;
        using ConcreteNLS = NonlinearSolver<tag>;
        return std::make_pair(std::make_unique<ConcreteNLS>(
                                  linear_solver, max_iter, recompute_jacobian,
                                  damping, damping_reduction),
                              tag);
    }
#ifdef USE_PETSC
    if (boost::iequals(type, "PETScSNES"))
    {
        auto prefix =
            config.getConfigParameter<std::string>("prefix", "");
        auto const tag = NonlinearSolverTag::Newton;
        using ConcreteNLS = PETScNonlinearSolver;
        return std::make_pair(std::make_unique<ConcreteNLS>(
                                  linear_solver, max_iter, std::move(prefix)),
                              tag);
    }
 
#endif
    OGS_FATAL("Unsupported nonlinear solver type '{:s}'.", type.c_str());
}
 
NonlinearSolver<NonlinearSolverTag::Picard>::~NonlinearSolver()
{
    if (_r_neq != nullptr)
    {
        NumLib::GlobalVectorProvider::provider.releaseVector(*_r_neq);
    }
}
 
NonlinearSolver<NonlinearSolverTag::Newton>::~NonlinearSolver()
{
    if (_r_neq != nullptr)
    {
        NumLib::GlobalVectorProvider::provider.releaseVector(*_r_neq);
    }
}
 
}  // namespace NumLib