.. _program_listing_file__tmp_ws_src_eigenpy_include_eigenpy_decompositions_sparse_cholmod_CholmodBase.hpp:

Program Listing for File CholmodBase.hpp
========================================

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

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

.. code-block:: cpp

   /*
    * Copyright 2024 INRIA
    */
   
   #ifndef __eigenpy_decomposition_sparse_cholmod_cholmod_base_hpp__
   #define __eigenpy_decomposition_sparse_cholmod_cholmod_base_hpp__
   
   #include "eigenpy/eigenpy.hpp"
   #include "eigenpy/eigen/EigenBase.hpp"
   #include "eigenpy/decompositions/sparse/SparseSolverBase.hpp"
   
   #include <Eigen/CholmodSupport>
   
   namespace eigenpy {
   
   template <typename CholdmodDerived>
   struct CholmodBaseVisitor
       : public boost::python::def_visitor<CholmodBaseVisitor<CholdmodDerived> > {
     typedef CholdmodDerived Solver;
   
     typedef typename CholdmodDerived::MatrixType MatrixType;
     typedef typename MatrixType::Scalar Scalar;
     typedef typename MatrixType::RealScalar RealScalar;
     typedef MatrixType CholMatrixType;
     typedef typename MatrixType::StorageIndex StorageIndex;
   
     template <class PyClass>
     void visit(PyClass &cl) const {
       cl.def("analyzePattern", &Solver::analyzePattern,
              bp::args("self", "matrix"),
              "Performs a symbolic decomposition on the sparcity of matrix.\n"
              "This function is particularly useful when solving for several "
              "problems having the same structure.")
   
           .def(EigenBaseVisitor<Solver>())
           .def(SparseSolverBaseVisitor<Solver>())
   
           .def("compute",
                (Solver & (Solver::*)(const MatrixType &matrix)) & Solver::compute,
                bp::args("self", "matrix"),
                "Computes the sparse Cholesky decomposition of a given matrix.",
                bp::return_self<>())
   
           .def("determinant", &Solver::determinant, bp::arg("self"),
                "Returns the determinant of the underlying matrix from the "
                "current factorization.")
   
           .def("factorize", &Solver::factorize, bp::args("self", "matrix"),
                "Performs a numeric decomposition of a given matrix.\n"
                "The given matrix must has the same sparcity than the matrix on "
                "which the symbolic decomposition has been performed.\n"
                "See also analyzePattern().")
   
           .def("info", &Solver::info, bp::arg("self"),
                "NumericalIssue if the input contains INF or NaN values or "
                "overflow occured. Returns Success otherwise.")
   
           .def("logDeterminant", &Solver::logDeterminant, bp::arg("self"),
                "Returns the log determinant of the underlying matrix from the "
                "current factorization.")
   
           .def("setShift", &Solver::setShift, (bp::args("self", "offset")),
                "Sets the shift parameters that will be used to adjust the "
                "diagonal coefficients during the numerical factorization.\n"
                "During the numerical factorization, the diagonal coefficients "
                "are transformed by the following linear model: d_ii = offset + "
                "d_ii.\n"
                "The default is the identity transformation with offset=0.",
                bp::return_self<>());
     }
   };
   
   }  // namespace eigenpy
   
   #endif  // ifndef __eigenpy_decomposition_sparse_cholmod_cholmod_base_hpp__