least_squares.hpp File Reference

Precision-generic full-column-rank least squares with recoverable diagnostics. More...

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

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Classes
struct	uni20::linalg::LeastSquaresResult< 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) { least_squares_with_info(std::forward<Args>(args)...); }
auto	uni20::linalg::least_squares (Args &&... args)
	Return a preserving least-squares 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::least_squares_with_info (A const &a, B const &b, LeastSquaresOptions< make_real_t< tensor_element_t< A > > > const &options={})
	Infer precision and select the ordinary host-result backends.
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::least_squares_with_info (A const &a, B const &b, Precision p, LeastSquaresOptions< make_real_t< tensor_element_t< A > > > const &options={})
	Use the ordinary backend selector for the owning host result.
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::least_squares_with_info (Backend &&backend, A const &a, B const &b, LeastSquaresOptions< make_real_t< tensor_element_t< A > > > const &options={})
	Infer working precision from input defaults, requiring matching finite defaults when both exist.
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::least_squares_with_info (Backend &&backend, A const &a, B const &b, Precision p, LeastSquaresOptions< make_real_t< tensor_element_t< A > > > const &options={})
	Minimize each column's Euclidean residual for m-by-n A with m >= n.

Detailed Description

Precision-generic full-column-rank least squares with recoverable diagnostics.