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# scipy.sparse.linalg.splu

`scipy.sparse.linalg.``splu`(A, permc_spec=None, diag_pivot_thresh=None, drop_tol=None, relax=None, panel_size=None, options={})

  Compute the LU decomposition of a sparse, square matrix.

  Parameters : | A : sparse matrix Sparse matrix to factorize. Should be in CSR or CSC format. permc_spec : str, optional How to permute the columns of the matrix for sparsity preservation. (default: ‘COLAMD’) `NATURAL`: natural ordering. `MMD_ATA`: minimum degree ordering on the structure of A^T A. `MMD_AT_PLUS_A`: minimum degree ordering on the structure of A^T+A. `COLAMD`: approximate minimum degree column ordering diag_pivot_thresh : float, optional Threshold used for a diagonal entry to be an acceptable pivot. See SuperLU user’s guide for details [SLU] drop_tol : float, optional (deprecated) No effect. relax : int, optional Expert option for customizing the degree of relaxing supernodes. See SuperLU user’s guide for details [SLU] panel_size : int, optional Expert option for customizing the panel size. See SuperLU user’s guide for details [SLU] options : dict, optional Dictionary containing additional expert options to SuperLU. See SuperLU user guide [SLU] (section 2.4 on the ‘Options’ argument) for more details. For example, you can specify `options=dict(Equil=False, IterRefine='SINGLE'))` to turn equilibration off and perform a single iterative refinement.
  Returns : | invA : scipy.sparse.linalg.dsolve._superlu.SciPyLUType Object, which has a `solve` method.

  See also

  `spilu`
    incomplete LU decomposition

  Notes

  This function uses the SuperLU library.

  References

  [SLU] | (1, 2, 3, 4, 5) SuperLU http://crd.lbl.gov/~xiaoye/SuperLU/

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