\(\left\lVert A \right\rVert\) (one, inf, fro, max)
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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.
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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.
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\(\left\lVert A \right\rVert\) (one, inf, fro, max)
◆ colNorms()
template<typename matrix_type >
void slate::colNorms |
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Norm |
in_norm, |
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matrix_type & |
A, |
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blas::real_type< typename matrix_type::value_type > * |
values, |
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Options const & |
opts |
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Distributed parallel matrix norm.
- Template Parameters
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- Parameters
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[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, \(\sqrt{ \sum_{i, j} \abs{ A_{i, j} }^2 }\)
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[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.
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◆ norm()
template<typename matrix_type >
blas::real_type< typename matrix_type::value_type > slate::norm |
( |
Norm |
in_norm, |
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matrix_type & |
A, |
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Options const & |
opts |
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) |
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Distributed parallel general matrix norm.
- Template Parameters
-
- 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, \(\sqrt{ \sum_{i, j} \abs{ A_{i, j} }^2 }\)
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[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.
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