# numpy.linalg.tensorinv¶

numpy.linalg.tensorinv(a, ind=2)[source]

Compute the ‘inverse’ of an N-dimensional array.

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. b : ndarray a‘s tensordot inverse, shape a.shape[:ind] + a.shape[ind:]. LinAlgError : If a is singular or not ‘square’ (in the above sense).

tensordot, tensorsolve

Examples

```>>> a = np.eye(4*6)
>>> a.shape = (4, 6, 8, 3)
>>> ainv = np.linalg.tensorinv(a, ind=2)
>>> ainv.shape
(8, 3, 4, 6)
>>> b = np.random.randn(4, 6)
>>> np.allclose(np.tensordot(ainv, b), np.linalg.tensorsolve(a, b))
True
```
```>>> a = np.eye(4*6)
>>> a.shape = (24, 8, 3)
>>> ainv = np.linalg.tensorinv(a, ind=1)
>>> ainv.shape
(8, 3, 24)
>>> b = np.random.randn(24)
>>> np.allclose(np.tensordot(ainv, b, 1), np.linalg.tensorsolve(a, b))
True
```

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