CompleteOrthogonalDecomposition.hpp

/*
 * Copyright 2024 INRIA
 */

#ifndef __eigenpy_decompositions_complete_orthogonal_decomposition_hpp__
#define __eigenpy_decompositions_complete_orthogonal_decomposition_hpp__

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

#include <Eigen/QR>

namespace eigenpy {

template <typename _MatrixType>
struct CompleteOrthogonalDecompositionSolverVisitor
 : public boost::python::def_visitor<
 CompleteOrthogonalDecompositionSolverVisitor<_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::CompleteOrthogonalDecomposition<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 "
 "CompleteOrthogonalDecomposition.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 complete orthogonal "
 "factorization from a given matrix.\n"
 "This constructor computes the complete "
 "orthogonal 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 "
 "associated with the complete orthogonal decomposition.\n"
 "It has only linear complexity (that is, O(n) where n is the "
 "dimension of the square matrix) as the complete orthogonal "
 "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 complete orthogonal "
 "decomposition.\n"
 "It has only linear complexity (that is, O(n) where n is the "
 "dimension of the square matrix) as the complete orthogonal "
 "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("dimensionOfKernel", &Self::dimensionOfKernel, bp::arg("self"),
 "Returns the dimension of the kernel of the matrix of which *this "
 "is the complete orthogonal decomposition.")
 .def("info", &Self::info, bp::arg("self"),
 "Reports whether the complete orthogonal factorization was "
 "successful.\n"
 "Note: This function always returns Success. It is provided for "
 "compatibility with other factorization routines.")
 .def("isInjective", &Self::isInjective, bp::arg("self"),
 "Returns true if the matrix associated with this complete "
 "orthogonal decomposition "
 "represents an injective linear map, i.e. has trivial kernel; "
 "false otherwise.\n"
 "\n"
 "Note: This method has to determine which pivots should be "
 "considered nonzero. For that, it uses the threshold value that "
 "you can control by calling setThreshold(threshold).")
 .def("isInvertible", &Self::isInvertible, bp::arg("self"),
 "Returns true if the matrix associated with the complete "
 "orthogonal decomposition "
 "is invertible.\n"
 "\n"
 "Note: This method has to determine which pivots should be "
 "considered nonzero. For that, it uses the threshold value that "
 "you can control by calling setThreshold(threshold).")
 .def("isSurjective", &Self::isSurjective, bp::arg("self"),
 "Returns true if the matrix associated with this complete "
 "orthogonal decomposition "
 "represents a surjective linear map; false otherwise.\n"
 "\n"
 "Note: This method has to determine which pivots should be "
 "considered nonzero. For that, it uses the threshold value that "
 "you can control by calling setThreshold(threshold).")
 .def("maxPivot", &Self::maxPivot, bp::arg("self"),
 "Returns the absolute value of the biggest pivot, i.e. the "
 "biggest diagonal coefficient of U.")
 .def("nonzeroPivots", &Self::nonzeroPivots, bp::arg("self"),
 "Returns the number of nonzero pivots in the complete orthogonal "
 "decomposition. "
 "Here nonzero is meant in the exact sense, not in a fuzzy sense. "
 "So that notion isn't really intrinsically interesting, but it is "
 "still useful when implementing algorithms.")
 .def("rank", &Self::rank, bp::arg("self"),
 "Returns the rank of the matrix associated with the complete "
 "orthogonal "
 "decomposition.\n"
 "\n"
 "Note: This method has to determine which pivots should be "
 "considered nonzero. For that, it uses the threshold value that "
 "you can control by calling setThreshold(threshold).")

 .def("setThreshold",
 (Self & (Self::*)(const RealScalar &)) & Self::setThreshold,
 bp::args("self", "threshold"),
 "Allows to prescribe a threshold to be used by certain methods, "
 "such as rank(), who need to determine when pivots are to be "
 "considered nonzero. This is not used for the complete orthogonal "
 "decomposition "
 "itself.\n"
 "\n"
 "When it needs to get the threshold value, Eigen calls "
 "threshold(). By default, this uses a formula to automatically "
 "determine a reasonable threshold. Once you have called the "
 "present method setThreshold(const RealScalar&), your value is "
 "used instead.\n"
 "\n"
 "Note: A pivot will be considered nonzero if its absolute value "
 "is strictly greater than |pivot| ⩽ threshold×|maxpivot| where "
 "maxpivot is the biggest pivot.",
 bp::return_self<>())
 .def("threshold", &Self::threshold, bp::arg("self"),
 "Returns the threshold that will be used by certain methods such "
 "as rank().")

 .def("matrixQTZ", &Self::matrixQTZ, bp::arg("self"),
 "Returns the matrix where the complete orthogonal decomposition "
 "is stored.",
 bp::return_value_policy<bp::copy_const_reference>())
 .def("matrixT", &Self::matrixT, bp::arg("self"),
 "Returns the matrix where the complete orthogonal decomposition "
 "is stored.",
 bp::return_value_policy<bp::copy_const_reference>())
 .def("matrixZ", &Self::matrixZ, bp::arg("self"),
 "Returns the matrix Z.")

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

 .def("pseudoInverse", pseudoInverse, bp::arg("self"),
 "Returns the pseudo-inverse of the matrix associated with the "
 "complete orthogonal "
 "decomposition.")

 .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 =
 "CompleteOrthogonalDecomposition" + scalar_name<Scalar>::shortname();
 expose(classname);
 }

 static void expose(const std::string &name) {
 bp::class_<Solver>(
 name.c_str(),
 "This class performs a rank-revealing complete orthogonal "
 "decomposition of a matrix A into matrices P, Q, T, and Z such that:\n"
 "AP=Q[T000]Z"
 "by using Householder transformations. Here, P is a permutation "
 "matrix, Q and Z are unitary matrices and T an upper triangular matrix "
 "of size rank-by-rank. A may be rank deficient.",
 bp::no_init)
 .def(CompleteOrthogonalDecompositionSolverVisitor())
 .def(IdVisitor<Solver>());
 }

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

} // namespace eigenpy

#endif // ifndef
 // __eigenpy_decompositions_complete_orthogonal_decomposition_hpp__