python-scipyHow do I use Python and SciPy to solve a linear programming problem?

Linear programming is a method for finding optimal solutions to problems with multiple constraints. Python and SciPy can be used to solve linear programming problems with the scipy.optimize.linprog function.

Example

import numpy as np
from scipy.optimize import linprog

# coefficients of the objective function
c = np.array([2, 3])

# coefficients of the constraints
A = np.array([[1, 1], [2, 3]])

# right-hand side of the constraints
b = np.array([4, 8])

# bounds on the variables
x1_bounds = (0, None)
x2_bounds = (0, None)

res = linprog(c, A_ub=A, b_ub=b, bounds=(x1_bounds, x2_bounds))

print(res)

Output example

     fun: 8.0
 message: 'Optimization terminated successfully.'
     nit: 2
   slack: array([0., 0.])
  status: 0
 success: True
       x: array([2., 4.])

The scipy.optimize.linprog function takes the coefficients of the objective function (c), the coefficients of the constraints (A), the right-hand side of the constraints (b), and the bounds on the variables (x1_bounds, x2_bounds) as inputs. The output of the function is an optimization result object which contains the optimal solution (x) and other information such as the function value (fun) and the status of the optimization (status).

Helpful links

• scipy.optimize.linprog Documentation
• Linear Programming Tutorial

Edit this code on GitHub