diff --git a/spec/extensions/linear_algebra_functions.md b/spec/extensions/linear_algebra_functions.md index 00e05ab86..f623b9db0 100644 --- a/spec/extensions/linear_algebra_functions.md +++ b/spec/extensions/linear_algebra_functions.md @@ -267,8 +267,8 @@ Computes the multiplicative inverse of a square matrix (or a stack of square mat - an array containing the multiplicative inverses. The returned array must have a floating-point data type determined by {ref}`type-promotion` and must have the same shape as `x`. -(function-linalg-lstsq)= -### linalg.lstsq(x1, x2, /, *, rtol=None) +(function-linalg-lstsq-solve)= +### linalg.lstsq_solve(x1, x2, /, *, rtol=None) Returns the least-squares solution to a linear matrix equation `Ax = b`. @@ -288,14 +288,9 @@ Returns the least-squares solution to a linear matrix equation `Ax = b`. #### Returns -- **out**: _Tuple\[ <array>, <array>, <array>, <array> ]_ - - - a namedtuple `(x, residuals, rank, s)` whose +- **out**: _<array>_ - - first element must have the field name `x` and must be an array containing the least-squares solution for each `MxN` matrix in `x1`. The array containing the solutions must have shape `(N, K)` and must have a floating-point data type determined by {ref}`type-promotion`. - - second element must have the field name `residuals` and must be an array containing the sum of squares residuals (i.e., the squared Euclidean 2-norm for each column in `b - Ax`). The array containing the residuals must have shape `(K,)` and must have a floating-point data type determined by {ref}`type-promotion`. - - third element must have the field name `rank` and must be an array containing the effective rank of each `MxN` matrix. The array containing the ranks must have shape `shape(x1)[:-2]` and must have an integer data type. - - fourth element must have the field name `s` and must be an array containing the singular values for each `MxN` matrix in `x1`. The array containing the singular values must have shape `(..., min(M, N))` and must have a floating-point data type determined by {ref}`type-promotion`. + - an array containing the least-squares solution for each `MxN` matrix in `x1`. The array containing the solutions must have shape `(N, K)` and must have a floating-point data type determined by {ref}`type-promotion`. (function-linalg-matmul)= ### linalg.matmul(x1, x2, /) pFad - Phonifier reborn

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