`numpy.linalg:tensorsolve`

source: /numpy/linalg/_linalg.py :281

Signature

def tensorsolve ( a , b , axes = None )

Summary

Solve the tensor equation a x = b for x.

Extended Summary

It is assumed that all indices of x are summed over in the product, together with the rightmost indices of a, as is done in, for example, tensordot(a, x, axes=x.ndim).

Parameters

a : array_like: Coefficient tensor, of shape b.shape + Q. Q, a tuple, equals the shape of that sub-tensor of a consisting of the appropriate number of its rightmost indices, and must be such that prod(Q) == prod(b.shape) (in which sense a is said to be 'square').
b : array_like: Right-hand tensor, which can be of any shape.
axes : tuple of ints, optional: Axes in a to reorder to the right, before inversion. If None (default), no reordering is done.

Returns

x : ndarray, shape Q

Raises

: LinAlgError: If a is singular or not 'square' (in the above sense).

Examples

import numpy as np
a = np.eye(2*3*4).reshape((2*3, 4, 2, 3, 4))
rng = np.random.default_rng()
b = rng.normal(size=(2*3, 4))
x = np.linalg.tensorsolve(a, b)
x.shape
np.allclose(np.tensordot(a, x, axes=3), b)

✓

Aliases

numpy.linalg.tensorsolve