Program Listing for File CholmodSupernodalLLT.hpp
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/*
* Copyright 2024 INRIA
*/
#ifndef __eigenpy_decomposition_sparse_cholmod_cholmod_supernodal_llt_hpp__
#define __eigenpy_decomposition_sparse_cholmod_cholmod_supernodal_llt_hpp__
#include "eigenpy/eigenpy.hpp"
#include "eigenpy/decompositions/sparse/cholmod/CholmodDecomposition.hpp"
#include "eigenpy/utils/scalar-name.hpp"
namespace eigenpy {
template <typename MatrixType_, int UpLo_ = Eigen::Lower>
struct CholmodSupernodalLLTVisitor
: public boost::python::def_visitor<
CholmodSupernodalLLTVisitor<MatrixType_, UpLo_> > {
typedef MatrixType_ MatrixType;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef Eigen::CholmodSupernodalLLT<MatrixType_, UpLo_> Solver;
template <class PyClass>
void visit(PyClass &cl) const {
cl
.def(CholmodBaseVisitor<Solver>())
.def(bp::init<>(bp::arg("self"), "Default constructor"))
.def(bp::init<MatrixType>(bp::args("self", "matrix"),
"Constructs and performs the LLT "
"factorization from a given matrix."))
;
}
static void expose() {
static const std::string classname =
"CholmodSupernodalLLT_" + scalar_name<Scalar>::shortname();
expose(classname);
}
static void expose(const std::string &name) {
bp::class_<Solver, boost::noncopyable>(
name.c_str(),
"A supernodal direct Cholesky (LLT) factorization and solver based on "
"Cholmod.\n\n"
"This class allows to solve for A.X = B sparse linear problems via a "
"supernodal LL^T Cholesky factorization using the Cholmod library."
"This supernodal variant performs best on dense enough problems, e.g., "
"3D FEM, or very high order 2D FEM."
"The sparse matrix A must be selfadjoint and positive definite. The "
"vectors or matrices X and B can be either dense or sparse.",
bp::no_init)
.def(CholmodSupernodalLLTVisitor());
}
};
} // namespace eigenpy
#endif // ifndef
// __eigenpy_decomposition_sparse_cholmod_cholmod_supernodal_llt_hpp__