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# torch.linalg.ldl_factor_ex

torch.linalg.ldl_factor_ex(A, *, hermitian=False, check_errors=False, out=None)

  This is a version of `ldl_factor()` that does not perform error checks unless `check_errors`= True. It also returns the `info` tensor returned by LAPACK’s sytrf. `info` stores integer error codes from the backend library. A positive integer indicates the diagonal element of D D D that is zero. Division by 0 will occur if the result is used for solving a system of linear equations. `info` filled with zeros indicates that the factorization was successful. If `check_errors=True` and `info` contains positive integers, then a RuntimeError is thrown.

  Note

  When the inputs are on a CUDA device, this function synchronizes only when `check_errors`= True.

  Warning

  This function is “experimental” and it may change in a future PyTorch release.

  Parameters:

    A (Tensor) – tensor of shape (*, n, n) where * is zero or more batch dimensions consisting of symmetric or Hermitian matrices.

  Keyword Arguments:

    - hermitian (bool, optional) – whether to consider the input to be Hermitian or symmetric. For real-valued matrices, this switch has no effect. Default: False.

    - check_errors (bool, optional) – controls whether to check the content of `info` and raise an error if it is non-zero. Default: False.

    - out (tuple, optional) – tuple of three tensors to write the output to. Ignored if None. Default: None.

  Returns:

    A named tuple (LD, pivots, info).

  Examples:

      >>> A = torch.randn(3, 3)
      >>> A = A @ A.mT # make symmetric
      >>> A
      tensor([[7.2079, 4.2414, 1.9428],
              [4.2414, 3.4554, 0.3264],
              [1.9428, 0.3264, 1.3823]])
      >>> LD, pivots, info = torch.linalg.ldl_factor_ex(A)
      >>> LD
      tensor([[ 7.2079,  0.0000,  0.0000],
              [ 0.5884,  0.9595,  0.0000],
              [ 0.2695, -0.8513,  0.1633]])
      >>> pivots
      tensor([1, 2, 3], dtype=torch.int32)
      >>> info
      tensor(0, dtype=torch.int32)

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