         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.
        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.

Options
-------
maxiter : int
   The maximum number of iterations to perform in either phase.
tol : float
    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.
disp : bool
    Set to ``True`` if indicators of optimization status are to be printed
    to the console each iteration.
maxupdate : int
    The maximum number of updates performed on the LU factorization.
    After this many updates is reached, the basis matrix is factorized
    from scratch.
mast : bool
    Minimize Amortized Solve Time. If enabled, the average time to solve
    a linear system using the basis factorization is measured. Typically,
    the average solve time will decrease with each successive solve after
    initial factorization, as factorization takes much more time than the
    solve operation (and updates). Eventually, however, the updated
    factorization becomes sufficiently complex that the average solve time
    begins to increase. When this is detected, the basis is refactorized
    from scratch. Enable this option to maximize speed at the risk of
    nondeterministic behavior. Ignored if ``maxupdate`` is 0.
pivot : "mrc" or "bland"
    Pivot rule: Minimum Reduced Cost (default) or Bland's rule. Choose
    Bland's rule if iteration limit is reached and cycling is suspected.
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
-------
x : 1-D array
    Solution vector.
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 : Numerical difficulties encountered
     5 : No constraints; turn presolve on
     6 : Guess x0 cannot be converted to a basic feasible solution

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