        thus a more robust least-squares solver is desired. This is sometimes
        needed as the solution is approached.
    sym_pos : bool
        True if the system matrix is symmetric positive definite
        Sometimes this needs to be set false as the solution is approached,
        even when the system should be symmetric positive definite, due to
        numerical difficulties.
    cholesky : bool
        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.

    r