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# scipy.linalg.svdvals

`scipy.linalg.``svdvals`(a, overwrite_a=False, check_finite=True)

  Compute singular values of a matrix.

  Parameters : | a : (M, N) array_like Matrix to decompose. overwrite_a : bool, optional Whether to overwrite a; may improve performance. Default is False. check_finite : boolean, optional Whether to check the input matrixes contain only finite numbers. Disabling may give a performance gain, but may result to problems (crashes, non-termination) if the inputs do contain infinities or NaNs.
  Returns : | s : (min(M, N),) ndarray The singular values, sorted in decreasing order.
  Raises : | LinAlgError : If SVD computation does not converge.

  See also

  `svd`
    Compute the full singular value decomposition of a matrix.
  `diagsvd`
    Construct the Sigma matrix, given the vector s.

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