IterativeSolverBase.hpp 4.63 KiB
/*
* Copyright 2017, Justin Carpentier, LAAS-CNRS
*
* This file is part of eigenpy.
* eigenpy is free software: you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation, either version 3 of
* the License, or (at your option) any later version.
* eigenpy 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 Lesser General Public License for more details. You should
* have received a copy of the GNU Lesser General Public License along
* with eigenpy. If not, see <http://www.gnu.org/licenses/>.
*/
#ifndef __eigenpy_iterative_solver_base_hpp__
#define __eigenpy_iterative_solver_base_hpp__
#include <boost/python.hpp>
#include <Eigen/Core>
#include "eigenpy/solvers/SparseSolverBase.hpp"
namespace eigenpy
{
namespace bp = boost::python;
template<typename IterativeSolver>
struct IterativeSolverVisitor
: public boost::python::def_visitor< IterativeSolverVisitor<IterativeSolver> >
{
typedef typename IterativeSolver::MatrixType MatrixType;
typedef Eigen::VectorXd VectorType;
template<class PyClass>
void visit(PyClass& cl) const
{
typedef IterativeSolver IS;
cl
.def(SparseSolverVisitor<IS>())
.def("error",&IS::error,"Returns the tolerance error reached during the last solve.\n"
"It is a close approximation of the true relative residual error |Ax-b|/|b|.")
.def("info",&IS::info,"Returns success if the iterations converged, and NoConvergence otherwise.")
.def("iterations",&IS::iterations,"Returns the number of iterations performed during the last solve.")
.def("maxIterations",&IS::maxIterations,"Returns the max number of iterations.\n"
"It is either the value setted by setMaxIterations or, by default, twice the number of columns of the matrix.")
.def("setMaxIterations",&IS::setMaxIterations,"Sets the max number of iterations.\n"
"Default is twice the number of columns of the matrix.",
bp::return_value_policy<bp::reference_existing_object>())
.def("tolerance",&IS::tolerance,"Returns he tolerance threshold used by the stopping criteria.")
.def("setTolerance",&IS::setTolerance,"Sets the tolerance threshold used by the stopping criteria.\n"
"This value is used as an upper bound to the relative residual error: |Ax-b|/|b|. The default value is the machine precision.",
bp::return_value_policy<bp::reference_existing_object>())
.def("analyzePattern",&analyzePattern,bp::arg("A"),"Initializes the iterative solver for the sparsity pattern of the matrix A for further solving Ax=b problems.\n"
"Currently, this function mostly calls analyzePattern on the preconditioner.\n"
"In the future we might, for instance, implement column reordering for faster matrix vector products.",
bp::return_value_policy<bp::reference_existing_object>())
.def("factorize",&factorize,bp::arg("A"),"Initializes the iterative solver with the numerical values of the matrix A for further solving Ax=b problems.\n"
"Currently, this function mostly calls factorize on the preconditioner.",
bp::return_value_policy<bp::reference_existing_object>())
.def("compute",&compute,bp::arg("A"),"Initializes the iterative solver with the numerical values of the matrix A for further solving Ax=b problems.\n"
"Currently, this function mostly calls factorize on the preconditioner.\n"
"In the future we might, for instance, implement column reordering for faster matrix vector products.",
bp::return_value_policy<bp::reference_existing_object>())
.def("solveWithGuess",&solveWithGuess,bp::args("b","x0"),
"Returns the solution x of Ax = b using the current decomposition of A and x0 as an initial solution.") ;
}
private:
static IterativeSolver & factorize(IterativeSolver & self, const MatrixType & m)
{
return self.factorize(m);
}
static IterativeSolver & compute(IterativeSolver & self, const MatrixType & m)
{
return self.compute(m);
}
static IterativeSolver & analyzePattern(IterativeSolver & self, const MatrixType & m)
{
return self.analyzePattern(m);
}
static VectorType solveWithGuess(IterativeSolver & self, const Eigen::VectorXd & b, const Eigen::VectorXd & x0)
{
return self.solveWithGuess(b,x0);
}
};
} // namespace eigenpy
#endif // ifndef __eigenpy_iterative_solver_base_hpp__