per iteration.

    Options
    -------
    maxiter : int (default: 5000)
       The maximum number of iterations to perform in either phase.
    disp : bool (default: False)
        Set to ``True`` if indicators of optimization status are to be printed
        to the console each iteration.
    presolve : bool (default: True)
        Presolve attempts to identify trivial infeasibilities,
        identify trivial unboundedness, and simplify the problem before
        sending it to the main solver. It is generally recommended
        to keep the default setting ``True``; set to ``False`` if
        presolve is to be disabled.
    tol : float (default: 1e-12)
        The tolerance which determines when a solution is "close enough" to
        zero in Phase 1 to be considered a basic feasible solution or close
        enough to positive to serve as an optimal solution.
    autoscale : bool (default: False)
        Set to ``True`` to automatically perform equilibration.
        Consider using this option if the numerical values in the
        constraints are separated by several orders of magnitude.
    rr : bool (default: True)
        Set to ``False`` to disable automatic redundancy removal.
    bland : bool
        If True, use Bland's anti-cycling rule [3]_ to choose pivots to
        prevent cycling. If False, choose pivots which should lead to a
        converged solution more quickly. The latter method is subject to
        cycling (non-convergence) in rare instances.
    unknown_options : dict
        Optional arguments not used by this particular solver. If
        `unknown_options` is non-empty a warning is issued listing all
        unused options.

    Returns
    -------
    res : OptimizeResult
        A :class:`scipy.optimize.OptimizeResult` consisting of the fields:

        x : 1-D array
            The values of the decision variables that minimizes the
            objective function while satisfying the constraints.
        fun : float
            The optimal value of the objective function ``c @ x``.
        slack : 1-D array
            The (nominally positive) values of the slack variables,
            ``b_ub - A_ub @ x``.
        con : 1-D array
            The (nominally zero) residuals of the equality constraints,
            ``b_eq - A_eq @ x``.
        success : bool
            ``True`` when the algorithm succeeds in finding an optimal
            solution.
        status : int
            An integer representing the exit status of the algorithm.

            ``0`` : Optimization terminated successfully.

            ``1`` : Iteration limit reached.

            ``2`` : Problem appears to be infeasible.

            ``3`` : Problem appears to be unbounded.

            ``4`` : Numerical difficulties encountered.

        message : str
            A string descriptor of the exit status of the algorithm.
        nit : int
            The total number of iterations performed in all phases.

    References
    ----------
    .. [1] Dantzig, George B., Linear programming and extensions. Rand
           Corporation Research Study Princeton Univ. Press, Princeton, NJ,
           1963
    .. [2] Hillier, S.H. and Lieberman, G.J. (1995), "Introduction to
           Mathematical Programming", McGraw-Hill, Chapter 4.
    .. [3] Bland, Robert G. New finite pivoting rules for the simplex method.
           Mathematics of Operations Research (2), 1977: pp. 103-107.
    Nr