HouseholderQR.hpp

/*
 * Copyright 2024 INRIA
 */

#ifndef __eigenpy_decompositions_houselholder_qr_hpp__
#define __eigenpy_decompositions_houselholder_qr_hpp__

#include "eigenpy/eigenpy.hpp"
#include "eigenpy/utils/scalar-name.hpp"

#include <Eigen/QR>

namespace eigenpy {

template <typename _MatrixType>
struct HouseholderQRSolverVisitor
 : public boost::python::def_visitor<
 HouseholderQRSolverVisitor<_MatrixType>> {
 typedef _MatrixType MatrixType;
 typedef typename MatrixType::Scalar Scalar;
 typedef typename MatrixType::RealScalar RealScalar;
 typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1, MatrixType::Options>
 VectorXs;
 typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic,
 MatrixType::Options>
 MatrixXs;
 typedef Eigen::HouseholderQR<MatrixType> Solver;
 typedef Solver Self;

 template <class PyClass>
 void visit(PyClass &cl) const {
 cl.def(bp::init<>(bp::arg("self"),
 "Default constructor.\n"
 "The default constructor is useful in cases in which the "
 "user intends to perform decompositions via "
 "HouseholderQR.compute(matrix)"))
 .def(bp::init<Eigen::DenseIndex, Eigen::DenseIndex>(
 bp::args("self", "rows", "cols"),
 "Default constructor with memory preallocation.\n"
 "Like the default constructor but with preallocation of the "
 "internal data according to the specified problem size. "))
 .def(bp::init<MatrixType>(
 bp::args("self", "matrix"),
 "Constructs a QR factorization from a given matrix.\n"
 "This constructor computes the QR factorization of the matrix "
 "matrix by calling the method compute()."))

 .def("absDeterminant", &Self::absDeterminant, bp::arg("self"),
 "Returns the absolute value of the determinant of the matrix of "
 "which *this is the QR decomposition.\n"
 "It has only linear complexity (that is, O(n) where n is the "
 "dimension of the square matrix) as the QR decomposition has "
 "already been computed.\n"
 "Note: This is only for square matrices.")
 .def("logAbsDeterminant", &Self::logAbsDeterminant, bp::arg("self"),
 "Returns the natural log of the absolute value of the determinant "
 "of the matrix of which *this is the QR decomposition.\n"
 "It has only linear complexity (that is, O(n) where n is the "
 "dimension of the square matrix) as the QR decomposition has "
 "already been computed.\n"
 "Note: This is only for square matrices. This method is useful to "
 "work around the risk of overflow/underflow that's inherent to "
 "determinant computation.")

 .def("matrixQR", &Self::matrixQR, bp::arg("self"),
 "Returns the matrix where the Householder QR decomposition is "
 "stored in a LAPACK-compatible way.",
 bp::return_value_policy<bp::copy_const_reference>())

 .def(
 "compute",
 (Solver & (Solver::*)(const Eigen::EigenBase<MatrixType> &matrix)) &
 Solver::compute,
 bp::args("self", "matrix"),
 "Computes the QR factorization of given matrix.",
 bp::return_self<>())

 .def("solve", &solve<MatrixXs>, bp::args("self", "B"),
 "Returns the solution X of A X = B using the current "
 "decomposition of A where B is a right hand side matrix.");
 }

 static void expose() {
 static const std::string classname =
 "HouseholderQR" + scalar_name<Scalar>::shortname();
 expose(classname);
 }

 static void expose(const std::string &name) {
 bp::class_<Solver>(
 name.c_str(),
 "This class performs a QR decomposition of a matrix A into matrices Q "
 "and R such that A=QR by using Householder transformations.\n"
 "Here, Q a unitary matrix and R an upper triangular matrix. The result "
 "is stored in a compact way compatible with LAPACK.\n"
 "\n"
 "Note that no pivoting is performed. This is not a rank-revealing "
 "decomposition. If you want that feature, use FullPivHouseholderQR or "
 "ColPivHouseholderQR instead.\n"
 "\n"
 "This Householder QR decomposition is faster, but less numerically "
 "stable and less feature-full than FullPivHouseholderQR or "
 "ColPivHouseholderQR.",
 bp::no_init)
 .def(HouseholderQRSolverVisitor())
 .def(IdVisitor<Solver>());
 }

 private:
 template <typename MatrixOrVector>
 static MatrixOrVector solve(const Solver &self, const MatrixOrVector &vec) {
 return self.solve(vec);
 }
};

} // namespace eigenpy

#endif // ifndef __eigenpy_decompositions_houselholder_qr_hpp__