CODEWITHSUNDEEP
pythonscipy
NewJ
NewJ

Reputation: 31

Maximize Optimization with two restraints using Scipy

I'm trying to solve a linear programming function with two restraints. Basically, I need to maximize profit with a restraint of capacity and budget.

import numpy as np
from scipy.optimize import minimize

"""

maximize g1*g1_profit + g2*g2_profit (or minimize -...)
st (g1*g1_price + g2*g2_price) <= budget
   (g1*g1_size + g2*g2_size) <= total_size
   
"""

budget = 10000

total_size = 50

g1_price = 300
g2_price = 600

g1_size = 2
g2_size = 1

g1_profit = 1200
g2_profit = 1000

def objective(x):
    global g1, g2
    g1 = x[0]
    g2 = x[1]
    return g1*g1_profit + g2*g2_profit

def constraint1(x):
    return -(g1*g1_price + g2*g2_price) + budget

def constraint2(x):
    return -(g1*g1_size + g2*g2_size) + total_size

x0 = [1,1]
print(-objective(x0))

cons1 = {'type': 'ineq', 'fun': constraint1}
cons2 = {'type': 'ineq', 'fun': constraint2}
cons = (cons1, cons2)

sol = minimize(objective,x0,method='SLSQP',constraints=cons)

print(sol)

I am getting this result:

print(sol)
     fun: -1.334563245874159e+38
     jac: array([1200.00002082, 1000.0000038 ])
 message: 'Singular matrix E in LSQ subproblem'
    nfev: 144
     nit: 48
    njev: 48
  status: 5
 success: False
       x: array([-6.56335026e+34, -5.46961214e+34])

So the success is False and the result is wrong. I did read optimize help page and I checked a few example online but I can't find any solution. Thanks in advance.

Upvotes: 0

Views: 370

Answers (1)

joni
joni

Reputation: 7157

First, please avoid global variables whenever possible. You can easily rewrite your functions as:

def objective(x):
    g1, g2 = x
    return g1*g1_profit + g2*g2_profit

def constraint1(x):
    g1, g2 = x
    return -(g1*g1_price + g2*g2_price) + budget

def constraint2(x):
    g1, g2 = x
    return -(g1*g1_size + g2*g2_size) + total_size

Then, you need to multiply the objective with -1.0 in order to transform your maximization problem into a minimization problem:

minimize(lambda x: -1.0*objective(x),x0, method='SLSQP', constraints=cons)

This gives me

     fun: -32222.222047310468
     jac: array([-1200., -1000.])
 message: 'Optimization terminated successfully'
    nfev: 6
     nit: 6
    njev: 2
  status: 0
 success: True
       x: array([22.22222214,  5.55555548])

Hence, the optimal objective function value is 32222.

Upvotes: 1

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