Lambda objective function for python linear optimisation

Question

I am trying to solve a problem where I need to optimize a linear function, but in practice this must be accessed through a wrapper function, I can solve this with the raw data, but due to implementation constraints I need a way to pass a function as the objective.

For example,

import numpy as np
from scipy.optimize import linprog

def objective(x):
    c = [-1, 4]
    return np.dot(c,x)
c = [-1, 4]
A = [[-3, 1], [1, 2]]
b = [6, 4]
x0_bounds = (None, None)
x1_bounds = (-3, None)
#This Works:
res = linprog(c, A_ub=A, b_ub=b, bounds=(x0_bounds, x1_bounds),
             options={"disp": True})
#This does not
res = linprog(objective, A_ub=A, b_ub=b, bounds=(x0_bounds, x1_bounds),
             options={"disp": True})

I could not see any appropriate resource in cvxopt, cvxpy or scipy. Any help is much appreciated.

Linear function can be fully defined by its coefficients so it's not clear why you need to pass the whole function instead of passing its coefficients. — serge_k
– serge_k, Commented Feb 25, 2016 at 7:47

Mathieu B · Accepted Answer · 2016-02-24 13:00:38Z

0

You noticed that linprog requires a coefficient array c, so if you have a function, you can simply deduce the c array:

import numpy as np

x = np.diag((1,1))
c = np.linalg.solve(x,objective(x))

res = linprog(c, A_ub=A, b_ub=b, bounds=(x0_bounds, x1_bounds),
             options={"disp": True})

edited Feb 24, 2016 at 13:00

answered Feb 24, 2016 at 12:42

Mathieu B

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