8000 Add specification for computing the number of non-zero singular values of a matrix (linalg: matrix_rank) by kgryte · Pull Request #128 · data-apis/array-api · GitHub
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Add specification for computing the number of non-zero singular values of a matrix (linalg: matrix_rank) #128

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Merged
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May 12, 2021
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Add specification for computing the number of non-zero singular values of a matrix (linalg: matrix_rank) #128

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Add matrix_rank specification
kgryte Feb 16, 2021
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kgryte Feb 16, 2021
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Reorder sentences
kgryte Feb 16, 2021
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Fix missing clause
kgryte Feb 16, 2021
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Rename keyword argument
kgryte Mar 4, 2021
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kgryte Mar 24, 2021
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Merge branch 'main' of https://github.com/pydata-apis/array-api into …
kgryte Mar 25, 2021
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Update dtype requirements
kgryte Mar 25, 2021
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kgryte Mar 25, 2021
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Move API to submodule
kgryte Apr 26, 2021
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@kgryte
kgryte committed Mar 25, 2021
commit a5d2c6188bbbd604b7ab9e95f7e84eef87fcb983
4 changes: 2 additions & 2 deletions spec/API_specification/linear_algebra_functions.md
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Expand Up @@ -162,13 +162,13 @@ Computes the rank (i.e., number of non-zero singular values) of a matrix (or a s

- **rtol**: _Optional\[ Union\[ float, <array> ] ]_

- relative tolerance for small singular values. Singular values less than or equal to `rtol * largest_singular_value` are set to zero. If a `float`, the value is equivalent to a zero-dimensional array having a floating-point data type determined by {ref}`type-promotion` rules (as applied to `x`) and must be broadcast against each matrix. If an `array`, must have a floating-point data type and must be compatible with `shape(x)[:-2]` (see {ref}`broadcasting`). If `None`, the default value is `max(M, N) * eps`, where `eps` must be the machine epsilon associated with the floating-point data type determined by {ref}`type-promotion` rules (as applied to `x`). Default: `None`.
- relative tolerance for small singular values. Singular values less than or equal to `rtol * largest_singular_value` are set to zero. If a `float`, the value is equivalent to a zero-dimensional array having a floating-point data type determined by {ref}`type-promotion` (as applied to `x`) and must be broadcast against each matrix. If an `array`, must have a floating-point data type and must be compatible with `shape(x)[:-2]` (see {ref}`broadcasting`). If `None`, the default value is `max(M, N) * eps`, where `eps` must be the machine epsilon associated with the floating-point data type determined by {ref}`type-promotion` (as applied to `x`). Default: `None`.

#### Returns

- **out**: _<array>_

- an array containing the ranks. The returned array must have a floating-point data type determined by {ref}`type-promotion` rules and must have shape `(...)` (i.e., must have a shape equal to `shape(x)[:-2]`).
- an array containing the ranks. The returned array must have a floating-point data type determined by {ref}`type-promotion` and must have shape `(...)` (i.e., must have a shape equal to `shape(x)[:-2]`).

(function-norm)=
### norm(x, /, *, axis=None, keepdims=False, ord=None)
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