Some fixes to the linear algebra functions

spec/API_specification/array_object.md

@@ -837,7 +837,9 @@ The `matmul` function must implement the same semantics as the built-in `@` oper
 #### Raises
 
 -   if either `self` or `other` is a zero-dimensional array.
--   if `self` is a one-dimensional array having shape `(N)`, `other` is a one-dimensional array having shape `(M)`, and `N != M`.
+-   if `self` is a one-dimensional array having shape `(K)`, `other` is a one-dimensional array having shape `(L)`, and `K != L`.
+-   if `self` is a one-dimensional array having shape `(K)`, `other` is an array having shape `(..., L, M)`, and `K != L`.
+-   if `self` is an array having shape `(..., N, K)`, `other` is a one-dimensional array having shape `(L)`, and `K != L`.
 -   if `self` is an array having shape `(..., M, K)`, `other` is an array having shape `(..., L, N)`, and `K != L`.
 
 (method-__mod__)=

spec/API_specification/linear_algebra_functions.md

@@ -52,7 +52,9 @@ The `matmul` function must implement the same semantics as the built-in `@` oper
 #### Raises
 
 -   if either `x1` or `x2` is a zero-dimensional array.
--   if `x1` is a one-dimensional array having shape `(N)`, `x2` is a one-dimensional array having shape `(M)`, and `N != M`.
+-   if `x1` is a one-dimensional array having shape `(K)`, `x2` is a one-dimensional array having shape `(L)`, and `K != L`.
+-   if `x1` is a one-dimensional array having shape `(K)`, `x2` is an array having shape `(..., L, M)`, and `K != L`.
+-   if `x1` is an array having shape `(..., N, K)`, `x2` is a one-dimensional array having shape `(L)`, and `K != L`.
 -   if `x1` is an array having shape `(..., M, K)`, `x2` is an array having shape `(..., L, N)`, and `K != L`.
 
 (function-matrix-transpose)=

spec/extensions/linear_algebra_functions.md

@@ -86,7 +86,7 @@ Returns the Cholesky decomposition of a symmetric positive-definite matrix (or a
 
 -   **x**: _<array>_
 
-    -   input array having shape `(..., M, M)` and whose innermost two dimensions form square matrices. Should have a floating-point data type.
+    -   input array having shape `(..., M, M)` and whose innermost two dimensions form square symmetric positive-definite matrices. Should have a floating-point data type.
 
 -   **upper**: _bool_
 
@@ -419,7 +419,7 @@ Computes the qr factorization of a matrix (or a stack of matrices), where `q` is
     -   a namedtuple `(q, r)` whose
 
         -   first element must have the field name `q` and must be an array whose shape depends on the value of `mode` and contain orthonormal matrices. If `mode` is `'complete'`, the array must have shape `(..., M, M)`. If `mode` is `'reduced'`, the array must have shape `(..., M, K)`, where `K = min(M, N)`. The first `x.ndim-2` dimensions must have the same size as those of the input `x`.
-        -   second element must have the field name `r` and must be an array whose shape depends on the value of `mode` and contain upper-triangular matrices. If `mode` is `'complete'`, the array must have shape `(..., M, M)`. If `mode` is `'reduced'`, the array must have shape `(..., K, N)`, where `K = min(M, N)`. The first `x.ndim-2` dimensions must have the same size as those of the input `x`.
+        -   second element must have the field name `r` and must be an array whose shape depends on the value of `mode` and contain upper-triangular matrices. If `mode` is `'complete'`, the array must have shape `(..., M, N)`. If `mode` is `'reduced'`, the array must have shape `(..., K, N)`, where `K = min(M, N)`. The first `x.ndim-2` dimensions must have the same size as those of the input `x`.
 
         Each returned array must have a floating-point data type determined by {ref}`type-promotion`.
 
@@ -495,7 +495,7 @@ Computes the singular value decomposition `A = USVh` of a matrix (or a stack of
     -   a namedtuple `(u, s, vh)` whose
 
         -   first element must have the field name `u` and must be an array whose shape depends on the value of `full_matrices` and contain unitary array(s) (i.e., the left singular vectors). The left singular vectors must be stored as columns. If `full_matrices` is `True`, the array must have shape `(..., M, M)`. If `full_matrices` is `False`, the array must have shape `(..., M, K)`, where `K = min(M, N)`. The first `x.ndim-2` dimensions must have the same shape as those of the input `x`.
-        -   second element must have the field name `s` and must be an array with shape `(..., K)` that contains the vector(s) of singular values of length `K`. For each vector, the singular values must be sorted in descending order by magnitude, such that `s[..., 0]` is the largest value, `s[..., 1]` is the second largest value, et cetera. The first `x.ndim-2` dimensions must have the same shape as those of the input `x`.
+        -   second element must have the field name `s` and must be an array with shape `(..., K)` that contains the vector(s) of singular values of length `K`, where `K = min(M, N)`. For each vector, the singular values must be sorted in descending order by magnitude, such that `s[..., 0]` is the largest value, `s[..., 1]` is the second largest value, et cetera. The first `x.ndim-2` dimensions must have the same shape as those of the input `x`.
         -   third element must have the field name `vh` and must be an array whose shape depends on the value of `full_matrices` and contain unitary array(s) (i.e., the right singular vectors). The right singular vectors must be stored as rows (i.e., the array is the adjoint). If `full_matrices` is `True`, the array must have shape `(..., N, N)`. If `full_matrices` is `False`, the array must have shape `(..., K, N)` where `K = min(M, N)`. The first `x.ndim-2` dimensions must have the same shape as those of the input `x`.
 
         Each returned array must have the same floating-point data type as `x`.
@@ -515,7 +515,7 @@ Computes the singular values of a matrix (or a stack of matrices) `x`.
 
 -   **out**: _<array>_
 
-    -   an array with shape `(..., K)` that contains the vector(s) of singular values of length `K`. For each vector, the singular values must be sorted in descending order by magnitude, such that `s[..., 0]` is the largest value, `s[..., 1]` is the second largest value, et cetera. The first `x.ndim-2` dimensions must have the same shape as those of the input `x`. The returned array must have the same floating-point data type as `x`.
+    -   an array with shape `(..., K)` that contains the vector(s) of singular values of length `K`, where `K = min(M, N)`. For each vector, the singular values must be sorted in descending order by magnitude, such that `s[..., 0]` is the largest value, `s[..., 1]` is the second largest value, et cetera. The first `x.ndim-2` dimensions must have the same shape as those of the input `x`. The returned array must have the same floating-point data type as `x`.
 
 (function-linalg-tensordot)=
 ### linalg.tensordot(x1, x2, /, *, axes=2)