svd_least_squares.hpp File Reference

Precision-generic minimum-norm SVD least squares with recoverable rank diagnostics. More...

#include <uni20/linalg/backends/lapack/svd_least_squares.hpp>
#include <uni20/linalg/precision.hpp>
#include <uni20/tensor/precision.hpp>

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Classes
struct	uni20::linalg::SvdLeastSquaresResult< S >
	Preserving least-squares result; solution and residuals are published only on success. More...

Namespaces
namespace	uni20
namespace	uni20::linalg

Functions
template<class... Args> requires requires(Args&&... args) { svd_least_squares_with_info(std::forward<Args>(args)...); }
auto	uni20::linalg::svd_least_squares (Args &&... args)
	Return a preserving truncated-SVD solution; numerical failure uses the ordinary terminal error policy.
template<RankedTensorView< 2 > A, RankedTensorView< 2 > B> requires RealOrComplex<tensor_element_t<A>> && std::same_as<tensor_element_t<A>, tensor_element_t<B>>
auto	uni20::linalg::svd_least_squares_with_info (A const &a, B const &b, Precision p, SvdLeastSquaresOptions< make_real_t< tensor_element_t< A > > > const &options={})
	Use the ordinary backend selector for the owning host result.
template<RankedTensorView< 2 > A, RankedTensorView< 2 > B> requires RealOrComplex<tensor_element_t<A>> && std::same_as<tensor_element_t<A>, tensor_element_t<B>>
auto	uni20::linalg::svd_least_squares_with_info (A const &a, B const &b, SvdLeastSquaresOptions< make_real_t< tensor_element_t< A > > > const &options={})
	Infer precision and select the ordinary host-result backends.
template<KernelBackendSelector Backend, RankedTensorView< 2 > A, RankedTensorView< 2 > B> requires RealOrComplex<tensor_element_t<A>> && std::same_as<tensor_element_t<A>, tensor_element_t<B>>
auto	uni20::linalg::svd_least_squares_with_info (Backend &&backend, A const &a, B const &b, Precision p, SvdLeastSquaresOptions< make_real_t< tensor_element_t< A > > > const &options={})
	Compute a minimum-norm solution after singular-value truncation for rectangular A.
template<KernelBackendSelector Backend, RankedTensorView< 2 > A, RankedTensorView< 2 > B> requires RealOrComplex<tensor_element_t<A>> && std::same_as<tensor_element_t<A>, tensor_element_t<B>>
auto	uni20::linalg::svd_least_squares_with_info (Backend &&backend, A const &a, B const &b, SvdLeastSquaresOptions< make_real_t< tensor_element_t< A > > > const &options={})
	Infer working precision from input defaults, requiring matching finite defaults when both exist.

Detailed Description

Precision-generic minimum-norm SVD least squares with recoverable rank diagnostics.