.. _program_listing_file__tmp_ws_src_eigenpy_include_eigenpy_decompositions_sparse_LDLT.hpp:

Program Listing for File LDLT.hpp
=================================

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

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

.. code-block:: cpp

   /*
    * Copyright 2024 INRIA
    */
   
   #ifndef __eigenpy_decomposition_sparse_ldlt_hpp__
   #define __eigenpy_decomposition_sparse_ldlt_hpp__
   
   #include "eigenpy/eigenpy.hpp"
   #include "eigenpy/decompositions/sparse/SimplicialCholesky.hpp"
   #include "eigenpy/utils/scalar-name.hpp"
   
   namespace eigenpy {
   
   template <typename _MatrixType, int _UpLo = Eigen::Lower,
             typename _Ordering =
                 Eigen::AMDOrdering<typename _MatrixType::StorageIndex> >
   struct SimplicialLDLTVisitor
       : public boost::python::def_visitor<
             SimplicialLDLTVisitor<_MatrixType, _UpLo, _Ordering> > {
     typedef SimplicialLDLTVisitor<_MatrixType, _UpLo, _Ordering> Visitor;
     typedef _MatrixType MatrixType;
   
     typedef Eigen::SimplicialLDLT<MatrixType> Solver;
     typedef typename MatrixType::Scalar Scalar;
     typedef typename MatrixType::RealScalar RealScalar;
     typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1, MatrixType::Options>
         DenseVectorXs;
     typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic,
                           MatrixType::Options>
         DenseMatrixXs;
   
     template <class PyClass>
     void visit(PyClass &cl) const {
       cl.def(bp::init<>(bp::arg("self"), "Default constructor"))
           .def(bp::init<MatrixType>(bp::args("self", "matrix"),
                                     "Constructs and performs the LDLT "
                                     "factorization from a given matrix."))
   
           .def("vectorD", &vectorD, bp::arg("self"),
                "Returns the diagonal vector D.")
           .def(SimplicialCholeskyVisitor<Solver>());
     }
   
     static void expose() {
       static const std::string classname =
           "SimplicialLDLT_" + scalar_name<Scalar>::shortname();
       expose(classname);
     }
   
     static void expose(const std::string &name) {
       bp::class_<Solver, boost::noncopyable>(
           name.c_str(),
           "A direct sparse LDLT Cholesky factorizations.\n\n"
           "This class provides a LDL^T Cholesky factorizations of sparse "
           "matrices that are selfadjoint and positive definite."
           "The factorization allows for solving A.X = B where X and B can be "
           "either dense or sparse.\n\n"
           "In order to reduce the fill-in, a symmetric permutation P is applied "
           "prior to the factorization such that the factorized matrix is P A "
           "P^-1.",
           bp::no_init)
           .def(SimplicialLDLTVisitor());
     }
   
    private:
     static DenseVectorXs vectorD(const Solver &self) { return self.vectorD(); }
   };
   
   }  // namespace eigenpy
   
   #endif  // ifndef __eigenpy_decomposition_sparse_ldlt_hpp__