ool
    True if the system is to be solved by Cholesky, rather than LU,
    decomposition. This is typically faster unless the problem is very
    small or prone to numerical difficulties.
pc : bool
    True if the predictor-corrector method of Mehrota is to be used. This
    is almost always (if not always) beneficial. Even though it requires
    the solution of an additional linear system, the factorization
    is typically (implicitly) reused so solution is efficient, and the
    number of algorithm iterations is typically reduced.
ip : bool
    True if the improved initial point suggestion due to [4] section 4.3
    is desired. It's unclear whether this is beneficial.
permc_spec : str (default = 'MMD_AT_PLUS_A')
    (Has effect only with ``sparse = True``, ``lstsq = False``, ``sym_pos =
    True``.) A matrix is factorized in each iteration of the algorithm.
    This option specifies how to permute the columns of the matrix for
    sparsity preservation. Acceptable values are:

    - ``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.

    This option can impact the convergence of the
    interior point algorithm; test different values to determine which
    performs best for your problem. For more information, refer to
    ``scipy.sparse.linalg.splu``.

Returns
-------
Search directions as defined in [4]

References
----------
.. [4] Andersen, Erling D., and Knud D. Andersen. "The MOSEK interior point
       optimizer for linear programming: an implementation of the
       homogeneous algorithm." High performance optimization. Springer US,
       2000. 197-232.

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