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linprog(method=’Simplex’)¶

scipy.optimize.linprog(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, bounds=None, method='simplex', callback=None, options={'disp': False, 'bland': False, 'tol': 1e-12, 'maxiter': 1000})

Solve the following linear programming problem via a two-phase simplex algorithm.

maximize: c^T * x

subject to: A_ub * x <= b_ub: A_eq * x == b_eq

Parameters:

Parameters:	c : array_like Coefficients of the linear objective function to be maximized. A_ub : array_like 2-D array which, when matrix-multiplied by x, gives the values of the upper-bound inequality constraints at x. b_ub : array_like 1-D array of values representing the upper-bound of each inequality constraint (row) in A_ub. A_eq : array_like 2-D array which, when matrix-multiplied by x, gives the values of the equality constraints at x. b_eq : array_like 1-D array of values representing the RHS of each equality constraint (row) in A_eq. bounds : array_like The bounds for each independent variable in the solution, which can take one of three forms:: None : The default bounds, all variables are non-negative. (lb, ub) : If a 2-element sequence is provided, the same lower bound (lb) and upper bound (ub) will be applied to all variables. [(lb_0, ub_0), (lb_1, ub_1), ...] : If an n x 2 sequence is provided, each variable x_i will be bounded by lb[i] and ub[i]. Infinite bounds are specified using -np.inf (negative) or np.inf (positive). callback : callable If a callback function is provide, it will be called within each iteration of the simplex algorithm. The callback must have the signature callback(xk, kwargs) where xk is the current solution vector and kwargs is a dictionary containing the following:: “tableau” : The current Simplex algorithm tableau “nit” : The current iteration. “pivot” : The pivot (row, column) used for the next iteration. “phase” : Whether the algorithm is in Phase 1 or Phase 2. “bv” : A structured array containing a string representation of each basic variable and its current value.
Returns:	A scipy.optimize.OptimizeResult consisting of the following fields: x : ndarray The independent variable vector which optimizes the linear programming problem. slack : ndarray The values of the slack variables. Each slack variable corresponds to an inequality constraint. If the slack is zero, then the corresponding constraint is active. success : bool Returns True if the algorithm succeeded in finding an optimal solution. 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 nit : int The number of iterations performed. message : str A string descriptor of the exit status of the optimization.

c : array_like

Coefficients of the linear objective function to be maximized.

A_ub : array_like

2-D array which, when matrix-multiplied by x, gives the values of the upper-bound inequality constraints at x.

b_ub : array_like

1-D array of values representing the upper-bound of each inequality constraint (row) in A_ub.

A_eq : array_like

2-D array which, when matrix-multiplied by x, gives the values of the equality constraints at x.

b_eq : array_like

1-D array of values representing the RHS of each equality constraint (row) in A_eq.

bounds : array_like

The bounds for each independent variable in the solution, which can take one of three forms:: None : The default bounds, all variables are non-negative. (lb, ub) : If a 2-element sequence is provided, the same

lower bound (lb) and upper bound (ub) will be applied to all variables.

[(lb_0, ub_0), (lb_1, ub_1), ...] : If an n x 2 sequence is provided,

each variable x_i will be bounded by lb[i] and ub[i].

Infinite bounds are specified using -np.inf (negative) or np.inf (positive).

callback : callable

If a callback function is provide, it will be called within each iteration of the simplex algorithm. The callback must have the signature callback(xk, **kwargs) where xk is the current solution vector and kwargs is a dictionary containing the following:: “tableau” : The current Simplex algorithm tableau “nit” : The current iteration. “pivot” : The pivot (row, column) used for the next iteration. “phase” : Whether the algorithm is in Phase 1 or Phase 2. “bv” : A structured array containing a string representation of each

basic variable and its current value.

Returns:

A scipy.optimize.OptimizeResult consisting of the following fields:

x : ndarray
    The independent variable vector which optimizes the linear
    programming problem.
slack : ndarray
    The values of the slack variables.  Each slack variable corresponds
    to an inequality constraint.  If the slack is zero, then the
    corresponding constraint is active.
success : bool
    Returns True if the algorithm succeeded in finding an optimal
    solution.
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
nit : int
    The number of iterations performed.
message : str
    A string descriptor of the exit status of the optimization.

See also

For documentation for the rest of the parameters, see scipy.optimize.linprog

Options:

Options:	maxiter : int The maximum number of iterations to perform. disp : bool If True, print exit status message to sys.stdout 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 to serve as an optimal solution. 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.

maxiter : int

The maximum number of iterations to perform.

disp : bool

If True, print exit status message to sys.stdout

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 to serve as an optimal solution.

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.

References

[R587]

Dantzig, George B., Linear programming and extensions. Rand Corporation Research Study Princeton Univ. Press, Princeton, NJ, 1963

[R588]

Hillier, S.H. and Lieberman, G.J. (1995), “Introduction to Mathematical Programming”, McGraw-Hill, Chapter 4.

[R589]

Bland, Robert G. New finite pivoting rules for the simplex method. Mathematics of Operations Research (2), 1977: pp. 103-107.

Examples

Consider the following problem:

Minimize: f = -1*x[0] + 4*x[1]

Subject to: -3*x[0] + 1*x[1] <= 6

1*x[0] + 2*x[1] <= 4: x[1] >= -3

where: -inf <= x[0] <= inf

This problem deviates from the standard linear programming problem. In standard form, linear programming problems assume the variables x are non-negative. Since the variables don’t have standard bounds where 0 <= x <= inf, the bounds of the variables must be explicitly set.

There are two upper-bound constraints, which can be expressed as

dot(A_ub, x) <= b_ub

The input for this problem is as follows:

>>> from scipy.optimize import linprog
>>> c = [-1, 4]
>>> A = [[-3, 1], [1, 2]]
>>> b = [6, 4]
>>> x0_bnds = (None, None)
>>> x1_bnds = (-3, None)
>>> res = linprog(c, A, b, bounds=(x0_bnds, x1_bnds))
>>> print(res)
     fun: -22.0
 message: 'Optimization terminated successfully.'
     nit: 1
   slack: array([ 39.,   0.])
  status: 0
 success: True
       x: array([ 10.,  -3.])