x``, gives the values of the equality
        constraints at ``x``.
    b : 1-D array
        1-D array of values representing the RHS of each equality constraint
        (row) in ``A`` (for standard form problem).
    c : 1-D array
        Coefficients of the linear objective function to be minimized (for
        standard form problem).
    c0 : float
        Constant term in objective function due to fixed (and eliminated)
        variables. (Purely for display.)
    alpha0 : float
        The maximal step size for Mehrota's predictor-corrector search
        direction; see :math:`\beta_3`of [4] Table 8.1
    beta : float
        The desired reduction of the path parameter :math:`\mu` (see  [6]_)
    maxiter : int
        The maximum number of iterations of the algorithm.
    disp : bool
        Set to ``True`` if indicators of optimization status are to be printed
        to the console each iteration.
    tol : float
        Termination tolerance; see [4]_ Section 4.5.
    sparse : bool
        Set to ``True`` if the problem is to be treated as sparse. However,
        the inputs ``A_eq`` and ``A_ub`` should nonetheless be provided as
        (dense) arrays rather than sparse matrices.
    lstsq : bool
        Set to ``True`` if the problem is expected to be very poorly
        conditioned. This should always be left as ``False`` unless severe
        numerical difficulties are frequently encountered, and a better option
        would be to improve the formulation of the problem.
    sym_pos : bool
        Leave ``True`` if the problem is expected to yield a well conditioned
        symmetric positive definite normal equation matrix (almost always).
    cholesky : bool
        Set to ``True`` if the normal equations are to be solved by explicit
        Cholesky decomposition followed by explicit forward/backward
        substitution. This is typically faster for moderate, dense problems
        that are numerically well-behaved.
    pc : bool
        Leave ``True`` if the predictor-corrector method of Mehrota is to be
        used. This is almost always (if not always) beneficial.
    ip : bool
        Set to ``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``.
    callback : callable, optional
        If a callback function is provided, it will be called within each
        iteration of the algorithm. The callback function must accept a single
        `scipy.optimize.OptimizeResult` consisting of the following fields:

            x : 1-D array
                Current solution vector
            fun : float
                Current value of the objective function
            success : bool
                True only when an algorithm has completed successfully,
                so this is always False as the callback function is called
                only while the algorithm is still iterating.
            slack : 1-D array
                The values of the slack variables. Each slack variable
                corresponds to an inequality constraint. If the slack is zero,
                the corresponding constraint is active.
            con : 1-D array
                The (nominally zero) residuals of the equality constraints,
                that is, ``b - A_eq @ x``
            phase : int
                The phase of the algorithm being executed. This is always
                1 for the interior-point method because it has only one phase.
            status : int
                For revised simplex, this is always 0 because if a different
                status is detected, the algorithm terminates.
            nit : int
                The number of iterations performed.
            message : str
                A string descriptor of the exit status of the optimization.
    postsolve_args : tuple
        Data needed by _postsolve to convert the solution to the standard-form
        problem into the solution to the original problem.

    Returns
    -------
    x_hat : float
        Solution vector (for standard form problem).
    status : int
        An integer representing the exit status of the optimization::

         0 : Optimization terminated successfully
         1 : Iteration limit reached
         2 : Problem appears to be infeasible
         3 : Problem appears to be unbounded
         4 : Serious numerical difficulties encountered

    message : str
        A string descriptor of the exit status of the optimization.
    iteration : int
        The number of iterations taken to solve the problem

    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.
    .. [6] Freund, Robert M. "Primal-Dual Interior-Point Methods for Linear
           Programming based on Newton's Method." Unpublished Course Notes,
           March 2004. Available 2/25/2017 at:
           https://ocw.mit.edu/courses/sloan-school-of-management/15-084j-nonlinear-programming-spring-2004/lecture-notes/lec14_int_pt_mthd.pdf

    r