.. _program_listing_file__tmp_ws_src_eigenpy_include_eigenpy_decompositions_sparse_cholmod_CholmodSupernodalLLT.hpp:

Program Listing for File CholmodSupernodalLLT.hpp
=================================================

|exhale_lsh| :ref:`Return to documentation for file <file__tmp_ws_src_eigenpy_include_eigenpy_decompositions_sparse_cholmod_CholmodSupernodalLLT.hpp>` (``/tmp/ws/src/eigenpy/include/eigenpy/decompositions/sparse/cholmod/CholmodSupernodalLLT.hpp``)

.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS

.. code-block:: cpp

   /*
    * 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__