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# torch.cholesky_inverse¶

torch.cholesky_inverse(input, upper=False, *, out=None)

Computes the inverse of a symmetric positive-definite matrix $A$ using its Cholesky factor $u$: returns matrix inv. The inverse is computed using LAPACK routines dpotri and spotri (and the corresponding MAGMA routines).

If upper is False, $u$ is lower triangular such that the returned tensor is

$inv = (uu^{{T}})^{{-1}}$

If upper is True or not provided, $u$ is upper triangular such that the returned tensor is

$inv = (u^T u)^{{-1}}$

Supports input of float, double, cfloat and cdouble dtypes. Also supports batches of matrices, and if $A$ is a batch of matrices then the output has the same batch dimensions.

Parameters
• input (Tensor) – the input tensor $A$ of size $(*, n, n)$, consisting of symmetric positive-definite matrices where $*$ is zero or more batch dimensions.

• upper (bool, optional) – flag that indicates whether to return a upper or lower triangular matrix. Default: False

Keyword Arguments

out (Tensor, optional) – the output tensor for inv

Example:

>>> a = torch.randn(3, 3)
>>> a = torch.mm(a, a.t()) + 1e-05 * torch.eye(3) # make symmetric positive definite
>>> u = torch.linalg.cholesky(a)
>>> a
tensor([[  0.9935,  -0.6353,   1.5806],
[ -0.6353,   0.8769,  -1.7183],
[  1.5806,  -1.7183,  10.6618]])
>>> torch.cholesky_inverse(u)
tensor([[ 1.9314,  1.2251, -0.0889],
[ 1.2251,  2.4439,  0.2122],
[-0.0889,  0.2122,  0.1412]])
>>> a.inverse()
tensor([[ 1.9314,  1.2251, -0.0889],
[ 1.2251,  2.4439,  0.2122],
[-0.0889,  0.2122,  0.1412]])
>>> a = torch.randn(3, 2, 2) # Example for batched input
>>> a = a @ a.mT + 1e-03 # make symmetric positive-definite
>>> l = torch.linalg.cholesky(a)
>>> z = l @ l.mT
>>> torch.dist(z, a)
tensor(3.5894e-07)


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