Linear Algebra Functions

Array API specification for linear algebra functions.

A conforming implementation of the array API standard must provide and support the following functions adhering to the following conventions.

  • Positional parameters must be positional-only parameters. Positional-only parameters have no externally-usable name. When a function accepting positional-only parameters is called, positional arguments are mapped to these parameters based solely on their order.

  • Optional parameters must be keyword-only arguments.

  • Broadcasting semantics must follow the semantics defined in Broadcasting .

  • Unless stated otherwise, functions must support the data types defined in Data Types .

  • Unless stated otherwise, functions must adhere to the type promotion rules defined in Type Promotion Rules .

  • Unless stated otherwise, floating-point operations must adhere to IEEE 754-2019.

Objects in API

matmul(x1, x2, /)

Computes the matrix product.

Note

The matmul function must implement the same semantics as the built-in @ operator (see PEP 465 ).

Parameters

  • x1 : <array>

    • first input array. Should have a numeric data type. Must have at least one dimension. If x1 is one-dimensional having shape (M,) and x2 has more than one dimension, x1 must be promoted to a two-dimensional array by prepending 1 to its dimensions (i.e., must have shape (1, M) ). After matrix multiplication, the prepended dimensions in the returned array must be removed. If x1 has more than one dimension (including after vector-to-matrix promotion), shape(x1)[:-2] must be compatible with shape(x2)[:-2] (after vector-to-matrix promotion) (see Broadcasting ). If x1 has shape (..., M, K) , the innermost two dimensions form matrices on which to perform matrix multiplication.

  • x2 : <array>

    • second input array. Should have a numeric data type. Must have at least one dimension. If x2 is one-dimensional having shape (N,) and x1 has more than one dimension, x2 must be promoted to a two-dimensional array by appending 1 to its dimensions (i.e., must have shape (N, 1) ). After matrix multiplication, the appended dimensions in the returned array must be removed. If x2 has more than one dimension (including after vector-to-matrix promotion), shape(x2)[:-2] must be compatible with shape(x1)[:-2] (after vector-to-matrix promotion) (see Broadcasting ). If x2 has shape (..., K, N) , the innermost two dimensions form matrices on which to perform matrix multiplication.

Returns

  • out : <array>

    • if both x1 and x2 are one-dimensional arrays having shape (N,) , a zero-dimensional array containing the inner product as its only element.

    • if x1 is a two-dimensional array having shape (M, K) and x2 is a two-dimensional array having shape (K, N) , a two-dimensional array containing the conventional matrix product and having shape (M, N) .

    • if x1 is a one-dimensional array having shape (K,) and x2 is an array having shape (..., K, N) , an array having shape (..., N) (i.e., prepended dimensions during vector-to-matrix promotion must be removed) and containing the conventional matrix product .

    • if x1 is an array having shape (..., M, K) and x2 is a one-dimensional array having shape (K,) , an array having shape (..., M) (i.e., appended dimensions during vector-to-matrix promotion must be removed) and containing the conventional matrix product .

    • if x1 is a two-dimensional array having shape (M, K) and x2 is an array having shape (..., K, N) , an array having shape (..., M, N) and containing the conventional matrix product for each stacked matrix.

    • if x1 is an array having shape (..., M, K) and x2 is a two-dimensional array having shape (K, N) , an array having shape (..., M, N) and containing the conventional matrix product for each stacked matrix.

    • if either x1 or x2 has more than two dimensions, an array having a shape determined by Broadcasting shape(x1)[:-2] against shape(x2)[:-2] and containing the conventional matrix product for each stacked matrix.

    The returned array must have a data type determined by Type Promotion Rules .

Raises

  • if either x1 or x2 is a zero-dimensional array.

  • 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, N) , and K != L .

  • if x1 is an array having shape (..., M, 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 .

matrix_transpose(x, /)

Transposes a matrix (or a stack of matrices) x .

Parameters

  • x : <array>

    • input array having shape (..., M, N) and whose innermost two dimensions form MxN matrices.

Returns

  • out : <array>

    • an array containing the transpose for each matrix and having shape (..., N, M) . The returned array must have the same data type as x .

tensordot(x1, x2, /, *, axes=2)

Returns a tensor contraction of x1 and x2 over specific axes.

Parameters

  • x1 : <array>

    • first input array. Should have a numeric data type.

  • x2 : <array>

    • second input array. Must be compatible with x1 for all non-contracted axes (see Broadcasting ). Should have a numeric data type.

      Note

      Contracted axes (dimensions) must not be broadcasted.

  • axes : Union[ int, Tuple[ Sequence[ int ], Sequence[ int ] ] ]

    • number of axes (dimensions) to contract or explicit sequences of axes (dimensions) for x1 and x2 , respectively.

      If axes is an int equal to N , then contraction must be performed over the last N axes of x1 and the first N axes of x2 in order. The size of each corresponding axis (dimension) must match. Must be nonnegative.

      • If N equals 0 , the result is the tensor (outer) product.

      • If N equals 1 , the result is the tensor dot product.

      • If N equals 2 , the result is the tensor double contraction (default).

      If axes is a tuple of two sequences (x1_axes, x2_axes) , the first sequence must apply to x and the second sequence to x2 . Both sequences must have the same length. Each axis (dimension) x1_axes[i] for x1 must have the same size as the respective axis (dimension) x2_axes[i] for x2 . Each sequence must consist of unique (nonnegative) integers that specify valid axes for each respective array.

Returns

  • out : <array>

    • an array containing the tensor contraction whose shape consists of the non-contracted axes (dimensions) of the first array x1 , followed by the non-contracted axes (dimensions) of the second array x2 . The returned array must have a data type determined by Type Promotion Rules .

vecdot(x1, x2, /, *, axis=-1)

Computes the (vector) dot product of two arrays.

Parameters

  • x1 : <array>

    • first input array. Should have a numeric data type.

  • x2 : <array>

    • second input array. Must be compatible with x1 (see Broadcasting ). Should have a numeric data type.

  • axis : int

    • axis over which to compute the dot product. Must be an integer on the interval [-N, N) , where N is the rank (number of dimensions) of the shape determined according to Broadcasting . If specified as a negative integer, the function must determine the axis along which to compute the dot product by counting backward from the last dimension (where -1 refers to the last dimension). By default, the function must compute the dot product over the last axis. Default: -1 .

Returns

  • out : <array>

    • if x1 and x2 are both one-dimensional arrays, a zero-dimensional containing the dot product; otherwise, a non-zero-dimensional array containing the dot products and having rank N-1 , where N is the rank (number of dimensions) of the shape determined according to Broadcasting . The returned array must have a data type determined by Type Promotion Rules .

Raises

  • if provided an invalid axis .

  • if the size of the axis over which to compute the dot product is not the same for both x1 and x2 .