### Navigation

- index
- modules |
- modules |
- next |
- previous |
- SciPy v0.9 Reference Guide (DRAFT) »
- Linear algebra (`scipy.linalg`) »

# scipy.linalg.svdvals

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

  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

### Navigation

- index
- modules |
- modules |
- next |
- previous |
- SciPy v0.9 Reference Guide (DRAFT) »
- Linear algebra (`scipy.linalg`) »