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// SPDX-FileCopyrightText: Copyright © DUNE Project contributors, see file LICENSE.md in module root
// SPDX-License-Identifier: LicenseRef-GPL-2.0-only-with-DUNE-exception
#ifndef DUNE_PYTHON_ISTL_SOLVER_HH
#define DUNE_PYTHON_ISTL_SOLVER_HH
#include <dune/common/typeutilities.hh>
#include <dune/istl/solver.hh>
#include <dune/istl/solvers.hh>
#include <dune/istl/preconditioners.hh>
#include <dune/python/istl/preconditioners.hh>
#include <dune/python/pybind11/pybind11.h>
namespace Dune
{
namespace Python
{
// registerInverseOperator
// -----------------------
template< class Solver, class... options >
inline void registerInverseOperator ( pybind11::class_< Solver, options... > cls )
{
typedef typename Solver::domain_type Domain;
typedef typename Solver::range_type Range;
using pybind11::operator""_a;
cls.def( "__call__", [] ( Solver &self, Domain &x, Range &b, double reduction ) {
InverseOperatorResult result;
self.apply( x, b, reduction, result );
return std::make_tuple( result.iterations, result.reduction, result.converged, result.conv_rate, result.elapsed );
}, "x"_a, "b"_a, "reduction"_a,
R"doc(
Solve linear system
Args:
x: solution of linear system
b: right hand side of the system
reduction: factor to reduce the defect by
Returns: (iterations, reduction, converged, conv_rate, elapsed)
iterations: number of iterations performed
reduction: actual factor, the error has been reduced by
converged: True, if the solver has achieved its reduction requirements
conv_rate: rate of convergence
elapsed: time in seconds used to solve the linear system
Note:
- If the reduction is omitted, the default value of the solver is used.
- For iterative solvers, the solution must be initialized to the starting point.
- The right hand side b will be replaced by the residual.
)doc" );
cls.def( "__call__", [] ( Solver &self, Domain &x, Range &b ) {
InverseOperatorResult result;
self.apply( x, b, result );
return std::make_tuple( result.iterations, result.reduction, result.converged, result.conv_rate, result.elapsed );
}, "x"_a, "b"_a );
cls.def_property_readonly( "category", [] ( const Solver &self ) { return self.category(); },
R"doc(
Obtain category of the linear solver
)doc" );
cls.def( "asPreconditioner", [] ( Solver &self ) {
return new InverseOperator2Preconditioner< Solver >( self );
}, pybind11::keep_alive< 0, 1 >(),
R"doc(
Convert linear solver into preconditioner
)doc" );
}
namespace detail
{
// registerEndomorphismSolvers
// ---------------------------
template< class X, class Y, class... options >
inline std::enable_if_t< std::is_same< X, Y >::value >
registerEndomorphismSolvers ( pybind11::module module, pybind11::class_< LinearOperator< X, Y >, options... >, PriorityTag< 1 > )
{
typedef Dune::InverseOperator< X, Y > Solver;
using pybind11::operator""_a;
pybind11::options opts;
opts.disable_function_signatures();
module.def( "LoopSolver", [] ( LinearOperator< X, X > &op, Preconditioner< X, X > &prec, double reduction, int maxit, int verbose ) {
return static_cast< Solver * >( new Dune::LoopSolver< X >( op, prec, reduction, maxit, verbose ) );
}, "operator"_a, "preconditioner"_a, "reduction"_a, "maxIterations"_a = std::numeric_limits< int >::max(), "verbose"_a = 0, pybind11::keep_alive< 0, 1 >(), pybind11::keep_alive< 0, 2 >(),
R"doc(
Loop solver
Args:
operator: operator to invert
preconditioner: preconditioner to use (i.e., apprixmate inverse of the operator)
reduction: factor to reduce the defect by
maxIterations: maximum number of iterations to perform
verbose: verbosity level (0 = quiet, 1 = summary, 2 = verbose)
Returns:
ISTL Loop solver
Note:
The loop solver will apply the preconditioner once in each step.
)doc" );
module.def( "GradientSolver", [] ( LinearOperator< X, X > &op, Preconditioner< X, X > &prec, double reduction, int maxit, int verbose ) {
return static_cast< Solver * >( new Dune::GradientSolver< X >( op, prec, reduction, maxit, verbose ) );
}, "operator"_a, "preconditioner"_a, "reduction"_a, "maxIterations"_a = std::numeric_limits< int >::max(), "verbose"_a = 0, pybind11::keep_alive< 0, 1 >(), pybind11::keep_alive< 0, 2 >(),
R"doc(
Gradient iterative solver
Args:
operator: operator to invert
preconditioner: preconditioner to use (i.e., apprixmate inverse of the operator)
reduction: factor to reduce the defect by
maxIterations: maximum number of iterations to perform
verbose: verbosity level (0 = quiet, 1 = summary, 2 = verbose)
Returns:
ISTL Gradient solver
Note:
This method is also know as steepest descend method.
)doc" );
module.def( "CGSolver", [] ( LinearOperator< X, X > &op, Preconditioner< X, X > &prec, double reduction, int maxit, int verbose ) {
return static_cast< Solver * >( new Dune::CGSolver< X >( op, prec, reduction, maxit, verbose ) );
}, "operator"_a, "preconditioner"_a, "reduction"_a, "maxIterations"_a = std::numeric_limits< int >::max(), "verbose"_a = 0, pybind11::keep_alive< 0, 1 >(), pybind11::keep_alive< 0, 2 >(),
R"doc(
Conjugate gradient iterative solver
Args:
operator: operator to invert
preconditioner: preconditioner to use (i.e., apprixmate inverse of the operator)
reduction: factor to reduce the defect by
maxIterations: maximum number of iterations to perform
verbose: verbosity level (0 = quiet, 1 = summary, 2 = verbose)
Returns:
ISTL Conjugate gradient solver
Note:
The conjucate gradient method can only be applied if the operator and the preconditioner are both symmetric and positive definite.
)doc" );
module.def( "BiCGSTABSolver", [] ( LinearOperator< X, X > &op, Preconditioner< X, X > &prec, double reduction, int maxit, int verbose ) {
return static_cast< Solver * >( new Dune::CGSolver< X >( op, prec, reduction, maxit, verbose ) );
}, "operator"_a, "preconditioner"_a, "reduction"_a, "maxIterations"_a = std::numeric_limits< int >::max(), "verbose"_a = 0, pybind11::keep_alive< 0, 1 >(), pybind11::keep_alive< 0, 2 >(),
R"doc(
Biconjugate gradient stabilized iterative solver
Args:
operator: operator to invert
preconditioner: preconditioner to use (i.e., apprixmate inverse of the operator)
reduction: factor to reduce the defect by
maxIterations: maximum number of iterations to perform
verbose: verbosity level (0 = quiet, 1 = summary, 2 = verbose)
Returns:
ISTL Biconjugate gradient stabilized solver
)doc" );
module.def( "MinResSolver", [] ( LinearOperator< X, X > &op, Preconditioner< X, X > &prec, double reduction, int maxit, int verbose ) {
return static_cast< Solver * >( new Dune::CGSolver< X >( op, prec, reduction, maxit, verbose ) );
}, "operator"_a, "preconditioner"_a, "reduction"_a, "maxIterations"_a = std::numeric_limits< int >::max(), "verbose"_a = 0, pybind11::keep_alive< 0, 1 >(), pybind11::keep_alive< 0, 2 >(),
R"doc(
Minimal residual iterative solver
Args:
operator: operator to invert
preconditioner: preconditioner to use (i.e., apprixmate inverse of the operator)
reduction: factor to reduce the defect by
maxIterations: maximum number of iterations to perform
verbose: verbosity level (0 = quiet, 1 = summary, 2 = verbose)
Returns:
ISTL Minimal residual solver
Note:
The minimal residual method can only be applied if the operator and the preconditioner are both symmetric.
)doc" );
}
template< class X, class Y, class... options >
inline std::enable_if_t< std::is_same< X, Y >::value >
registerEndomorphismSolvers ( pybind11::module module, pybind11::class_< LinearOperator< X, Y >, options... >, PriorityTag< 0 > )
{}
template< class X, class Y, class... options >
inline void registerEndomorphismSolvers ( pybind11::module module, pybind11::class_< LinearOperator< X, Y >, options... > cls )
{
registerEndomorphismSolvers( module, cls, PriorityTag< 42 >() );
}
} // namespace detail
// registerSolvers
// ---------------
template< class X, class Y, class... options >
inline void registerSolvers ( pybind11::module module, pybind11::class_< LinearOperator< X, Y >, options... > cls )
{
typedef Dune::InverseOperator< X, Y > Solver;
using pybind11::operator""_a;
pybind11::options opts;
opts.disable_function_signatures();
pybind11::class_< Solver > clsSolver( module, "InverseOperator" );
registerInverseOperator( clsSolver );
detail::registerEndomorphismSolvers( module, cls );
module.def( "RestartedGMResSolver", [] ( LinearOperator< X, Y > &op, Preconditioner< X, Y > &prec, double reduction, int restart, int maxit, int verbose ) {
return static_cast< Solver * >( new Dune::RestartedGMResSolver< X, Y >( op, prec, reduction, restart, maxit, verbose ) );
}, "operator"_a, "preconditioner"_a, "reduction"_a, "restart"_a, "maxIterations"_a = std::numeric_limits< int >::max(), "verbose"_a = 0, pybind11::keep_alive< 0, 1 >(), pybind11::keep_alive< 0, 2 >(),
R"doc(
Restarted generalized minimal residual iterative solver
Args:
operator: operator to invert
preconditioner: preconditioner to use (i.e., apprixmate inverse of the operator)
reduction: factor to reduce the defect by
restart: number of iterations before restart
maxIterations: maximum number of iterations to perform
verbose: verbosity level (0 = quiet, 1 = summary, 2 = verbose)
Returns:
ISTL Restarted generalized minimal residual solver
Note:
The restarted generalized minimal residual method holds restart many vectors in memory during application.
This can lead to a large memory consumption.
)doc" );
}
} // namespace Python
} // namespace Dune
#endif // #ifndef DUNE_PYTHON_ISTL_SOLVER_HH
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