binary80.cpp File Reference

#include "binary80.hpp"
#include "decompositions_impl.hpp"
#include "diagnostics_impl.hpp"
#include <cmath>
#include <mpblas_binary80.h>
#include <mplapack_binary80.h>
#include <uni20/backend/lapack/lapack_error_presentation.hpp>
#include <algorithm>
#include <limits>
#include <stdexcept>
#include <type_traits>
#include <utility>

Include dependency graph for binary80.cpp:

Namespaces
namespace	uni20
namespace	uni20::mplapack

Functions
void	uni20::mplapack::eigen (std::size_t n, std::span< float80 > a, std::span< complex160 > values, std::span< complex160 > vectors, bool compute_vectors, Precision p)
	Compute unsorted complex eigenvalues and optional right eigenvectors, destroying a.
void	uni20::mplapack::eigh (std::size_t n, std::span< float80 > a, std::span< float80 > values, bool vectors, char triangle, Precision p)
	Compute ascending eigenvalues and optionally overwrite a with eigenvector columns.
float80	uni20::mplapack::gecon (std::size_t n, std::span< float80 const > a, float80 const &anorm, char norm, Precision p)
	Estimate reciprocal condition from packed successful LU and the original matrix norm.
std::size_t	uni20::mplapack::gelss (std::size_t m, std::size_t n, std::size_t nrhs, std::span< float80 > a, std::span< float80 > b, std::span< float80 > values, float80 const &cutoff, std::size_t &rank, Precision p)
	Packed GELSS; B has max(m,n) rows, values has min(m,n) entries.
void	uni20::mplapack::gemm (std::size_t m, std::size_t n, std::size_t k, complex160 const &alpha, std::span< complex160 const > a, std::span< complex160 const > b, complex160 const &beta, std::span< complex160 > c)
	Complex binary80 counterpart of the packed GEMM adapter.
void	uni20::mplapack::gemm (std::size_t m, std::size_t n, std::size_t k, float80 const &alpha, std::span< float80 const > a, std::span< float80 const > b, float80 const &beta, std::span< float80 > c)
	Packed column-major binary80 GEMM; provider representations stay private.
std::size_t	uni20::mplapack::getrf (std::size_t n, std::span< float80 > a, std::vector< std::size_t > &pivots)
	Factor a square binary80 matrix, returning zero or a one-based singular pivot.
void	uni20::mplapack::getrs (std::size_t n, std::size_t nrhs, std::span< float80 const > a, std::span< std::size_t const > pivots, std::span< float80 > b)
	Solve from read-only binary80 LU factors and zero-based pivots.
void	uni20::mplapack::lq (std::size_t m, std::size_t n, std::span< float80 > a, std::span< float80 > q, std::span< float80 > triangular, Precision p)
	Overwrite m-by-n input work and return Q (k-by-n) and L (m-by-k), k=min(m,n).
void	uni20::mplapack::qr (std::size_t m, std::size_t n, std::span< float80 > a, std::span< float80 > q, std::span< float80 > triangular, Precision p)
	Overwrite m-by-n input work and return Q (m-by-k) and R (k-by-n), k=min(m,n).
void	uni20::mplapack::svd (std::size_t m, std::size_t n, std::span< float80 > a, std::span< float80 > values, std::span< float80 > u, std::span< float80 > vh, char jobu, char jobvt, Precision p)
	Compute descending singular values and requested U/Vh factors, destroying a.