jax.numpy.linalg.tensorinv
jax.numpy.linalg.tensorinv#
- jax.numpy.linalg.tensorinv(a, ind=2)[source]#
Compute the ‘inverse’ of an N-dimensional array.
LAX-backend implementation of
numpy.linalg.tensorinv().Original docstring below.
The result is an inverse for a relative to the tensordot operation
tensordot(a, b, ind), i. e., up to floating-point accuracy,tensordot(tensorinv(a), a, ind)is the “identity” tensor for the tensordot operation.- Parameters
a (array_like) – Tensor to ‘invert’. Its shape must be ‘square’, i. e.,
prod(a.shape[:ind]) == prod(a.shape[ind:]).ind (int, optional) – Number of first indices that are involved in the inverse sum. Must be a positive integer, default is 2.
- Returns
b – a’s tensordot inverse, shape
a.shape[ind:] + a.shape[:ind].- Return type