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Add specification for returning the least-squares solution to a linear matrix equation (linalg: lstsq) #119

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Merged
merged 18 commits into from
May 12, 2021
Merged

Add specification for returning the least-squares solution to a linear matrix equation (linalg: lstsq) #119

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Stub spec
kgryte Jan 25, 2021
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Document keyword
kgryte Jan 25, 2021
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Update spec
kgryte Jan 25, 2021
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kgryte Feb 1, 2021
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kgryte Feb 15, 2021
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Rename keyword argument and add support for setting tolerance to a float
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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Rename keyword argument
kgryte Mar 4, 2021
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Fix name
kgryte Mar 4, 2021
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kgryte Mar 24, 2021
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Add support for providing an ordinate vector
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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Update dtype requirements
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kgryte Apr 26, 2021
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@kgryte
kgryte committed Feb 16, 2021
commit 37e55589f350a7400a552b248d8d2c35e39a8e50
2 changes: 1 addition & 1 deletion spec/API_specification/linear_algebra_functions.md
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Expand Up @@ -151,7 +151,7 @@ Returns the least-squares solution to a linear matrix equation `Ax = b`.

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

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

#### Returns

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