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# scipy.linalg.svdvals

`scipy.linalg.``svdvals`(a, overwrite_a=0)

  Compute singular values of a matrix.

  Parameters: | a : array, shape (M, N) Matrix to decompose overwrite_a : boolean Whether data in a is overwritten (may improve performance)
  Returns: | s: array, shape (K,) : The singular values, sorted so that s[i] >= s[i+1]. K = min(M, N) Raises LinAlgError if SVD computation does not converge :

  See also

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

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