#### Previous topic

scipy.optimize.diagbroyden

#### Next topic

scipy.optimize.linprog_verbose_callback

# scipy.optimize.linprog

`scipy.optimize.``linprog`(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, bounds=None, method='simplex', callback=None, options=None)

  Minimize a linear objective function subject to linear equality and inequality constraints.

  Linear Programming is intended to solve the following problem form:

  Minimize: c^T * x

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

  Parameters:

  c : array_like

  > Coefficients of the linear objective function to be minimized.

  A_ub : array_like, optional

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

  b_ub : array_like, optional

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

  A_eq : array_like, optional

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

  b_eq : array_like, optional

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

  bounds : sequence, optional

  > `(min, max)` pairs for each element in `x`, defining the bounds on that parameter. Use None for one of `min` or `max` when there is no bound in that direction. By default bounds are `(0, None)` (non-negative) If a sequence containing a single tuple is provided, then `min` and `max` will be applied to all variables in the problem.

  method : str, optional

  > Type of solver. At this time only ‘simplex’ is supported (see here).

  callback : callable, optional

  > 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.
  >     "basis" : The indices of the columns of the basic variables.

  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 show_options(‘linprog’).

  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

  `show_options`
    Additional options accepted by the solvers

  Notes

  This section describes the available solvers that can be selected by the ‘method’ parameter. The default method is Simplex.

  Method Simplex uses the Simplex algorithm (as it relates to Linear Programming, NOT the Nelder-Mead Simplex) [R147], [R148]. This algorithm should be reasonably reliable and fast.

  New in version 0.15.0.

  References

  [R147] | (1, 2) Dantzig, George B., Linear programming and extensions. Rand Corporation Research Study Princeton Univ. Press, Princeton, NJ, 1963

  [R148] | (1, 2) Hillier, S.H. and Lieberman, G.J. (1995), “Introduction to Mathematical Programming”, McGraw-Hill, Chapter 4.

  [R149] | 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:

      >>> c = [-1, 4]
      >>> A = [[-3, 1], [1, 2]]
      >>> b = [6, 4]
      >>> x0_bounds = (None, None)
      >>> x1_bounds = (-3, None)
      >>> from scipy.optimize import linprog
      >>> res = linprog(c, A_ub=A, b_ub=b, bounds=(x0_bounds, x1_bounds),
      ...               options={"disp": True})
      Optimization terminated successfully.
           Current function value: -22.000000
           Iterations: 1
      >>> print(res)
           fun: -22.0
       message: 'Optimization terminated successfully.'
           nit: 1
         slack: array([ 39.,   0.])
        status: 0
       success: True
             x: array([ 10.,  -3.])

  Note the actual objective value is 11.428571. In this case we minimized the negative of the objective function.