ctive function to be minimized.
    A_ub : 2D array, optional
        The inequality constraint matrix. Each row of ``A_ub`` specifies the
        coefficients of a linear inequality constraint on ``x``.
    b_ub : 1D array, optional
        The inequality constraint vector. Each element represents an
        upper bound on the corresponding value of ``A_ub @ x``.
    A_eq : 2D array, optional
        The equality constraint matrix. Each row of ``A_eq`` specifies the
        coefficients of a linear equality constraint on ``x``.
    b_eq : 1D array, optional
        The equality constraint vector. Each element of ``A_eq @ x`` must equal
        the corresponding element of ``b_eq``.
    bounds : 2D array
        The bounds of ``x``, as ``min`` and ``max`` pairs, one for each of the N
        elements of ``x``. The N x 2 array contains lower bounds in the first
        column and upper bounds in the 2nd. Unbounded variables have lower
        bound -np.inf and/or upper bound np.inf.
    x0 : 1D array, optional
        Guess values of the decision variables, which will be refined by
        the optimization algorithm. This argument is currently used only by the
        'revised simplex' method, and can only be used if `x0` represents a
        basic feasible solution.

options : dict, optional
    A dictionary of solver options. All methods accept the following
    generic options:

        maxiter : int
            Maximum number of iterations to perform.
        disp : bool
            Set to True to print convergence messages.

    For method-specific options, see :func:`show_options('linprog')`.

)