qr.hpp File Reference

Async Tensor wrappers for preserving and consuming reduced real or complex QR factorization. More...

#include <uni20/async/async.hpp>
#include <uni20/async/debug_scheduler.hpp>
#include <uni20/linalg/ops/qr.hpp>
#include <type_traits>
#include <utility>

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Namespaces
namespace	uni20
namespace	uni20::linalg

Functions
template<class MatrixTensor > requires uni20::OwningTensor<MatrixTensor> && uni20::MutableRankedTensorView<MatrixTensor, 2> && detail::DecompositionScalar<uni20::tensor_element_t<MatrixTensor>>
auto	uni20::linalg::qr (async::Async< MatrixTensor > &&matrix, std::optional< Precision > requested={})
	Schedule a consuming reduced real or complex QR factorization using the static Tensor selector.
template<uni20::RankedTensorView< 2 > MatrixTensor> requires detail::DecompositionScalar<uni20::tensor_element_t<MatrixTensor>>
auto	uni20::linalg::qr (async::Async< MatrixTensor > const &matrix, std::optional< Precision > requested={})
	Schedule a preserving reduced real or complex QR factorization using the static Tensor selector.
template<KernelBackendSelector BackendSelector, class MatrixTensor > requires uni20::OwningTensor<MatrixTensor> && uni20::MutableRankedTensorView<MatrixTensor, 2> && detail::DecompositionScalar<uni20::tensor_element_t<MatrixTensor>>
auto	uni20::linalg::qr (BackendSelector selector, async::Async< MatrixTensor > &&matrix, std::optional< Precision > requested={})
	Schedule a consuming reduced real or complex QR factorization through an explicit selector.
template<KernelBackendSelector BackendSelector, uni20::RankedTensorView< 2 > MatrixTensor> requires detail::DecompositionScalar<uni20::tensor_element_t<MatrixTensor>>
auto	uni20::linalg::qr (BackendSelector selector, async::Async< MatrixTensor > const &matrix, std::optional< Precision > requested={})
	Schedule a preserving reduced real or complex QR factorization through an explicit selector.

Detailed Description

Async Tensor wrappers for preserving and consuming reduced real or complex QR factorization.

Precision may be inferred from the input matrix or supplied explicitly. Fixed results require their native precision; runtime decompositions require finite precision. Owning results record the selected default. Inference occurs only after the input epoch is readable.