Driver

\(\left\lVert A \right\rVert\) (one, inf, fro, max) More...

Functions
template<typename matrix_type >
void	slate::colNorms (Norm in_norm, matrix_type &A, blas::real_type< typename matrix_type::value_type > *values, Options const &opts)
	Distributed parallel matrix norm.
template<typename matrix_type >
blas::real_type< typename matrix_type::value_type >	slate::norm (Norm in_norm, matrix_type &A, Options const &opts)
	Distributed parallel general matrix norm.

Detailed Description

\(\left\lVert A \right\rVert\) (one, inf, fro, max)

Function Documentation

colNorms()

template<typename matrix_type >

void slate::colNorms	(	Norm	in_norm,
		matrix_type &	A,
		blas::real_type< typename matrix_type::value_type > *	values,
		Options const &	opts
	)

Distributed parallel matrix norm.

Template Parameters

matrix_type

Any SLATE matrix type: Matrix, SymmetricMatrix, HermitianMatrix, TriangularMatrix, etc.

Parameters

[in]	in_norm	Norm to compute: Norm::Max: maximum element, \(\max_{i, j} \abs{ A_{i, j} }\) Norm::One: maximum column sum, \(\max_j \sum_i \abs{ A_{i, j} }\) Norm::Inf: maximum row sum, \(\max_i \sum_j \abs{ A_{i, j} }\) For symmetric and Hermitian matrices, the One and Inf norms are the same. Norm::Fro: Frobenius norm, \(\sqrt{ \sum_{i, j} \abs{ A_{i, j} }^2 }\)
[in]	A	The matrix A.
[out]	values	todo: undocumented.
[in]	opts	Additional options, as map of name = value pairs. Possible options: Option::Target: Implementation to target. Possible values: HostTask: OpenMP tasks on CPU host [default]. HostNest: nested OpenMP parallel for loop on CPU host. Devices: batched BLAS on GPU device.

norm()

template<typename matrix_type >

blas::real_type< typename matrix_type::value_type > slate::norm	(	Norm	in_norm,
		matrix_type &	A,
		Options const &	opts
	)

Distributed parallel general matrix norm.

Template Parameters

matrix_type

Any SLATE matrix type: Matrix, SymmetricMatrix, HermitianMatrix, TriangularMatrix, etc.

Parameters

[in]	in_norm	Norm to compute: Norm::Max: maximum element, \(\max_{i, j} \abs{ A_{i, j} }\) Norm::One: maximum column sum, \(\max_j \sum_i \abs{ A_{i, j} }\) Norm::Inf: maximum row sum, \(\max_i \sum_j \abs{ A_{i, j} }\) For symmetric and Hermitian matrices, the One and Inf norms are the same. Norm::Fro: Frobenius norm, \(\sqrt{ \sum_{i, j} \abs{ A_{i, j} }^2 }\)
[in]	A	The matrix A.
[in]	opts	Additional options, as map of name = value pairs. Possible options: Option::Target: Implementation to target. Possible values: HostTask: OpenMP tasks on CPU host [default]. HostNest: nested OpenMP parallel for loop on CPU host. Devices: batched BLAS on GPU device.