CODEWITHSUNDEEP
pythonscipy
Inna
Inna

Reputation: 1

Python SciPy linprog optimization fails with status 3

Trying to minimize a simple linear function with linprog. The coefficients are the elements of arr2 multiplied by -1. There are only inequality constraints for each variable, such as -1 <= x1 <= 1, -2 <= x2 <= 2 and so on.

If a choose not to specify bounds in linprog:

from scipy.optimize import linprog
import numpy as np
import pandas as pd

numdim = 28

arr1 = np.ones(numdim)
arr1 = - arr1

arr2 = np.array([
19.53,
128.97,
3538,
931.8,
0.1825,
150.88,
10315,
0.8109,
3.9475,
3022,
31.77,
10323,
110.93,
220,
2219.5,
119.2,
703.6,
616,
338,
84.67,
151.13,
111.28,
29.515,
29.67,
158800,
167.15,
0.06802,
1179
])

constr_a = []

for i in range(numdim):

    constr_default = np.zeros(numdim)
    constr_default[i] = 1
    constr_a.append(constr_default)

for i in range(numdim):

    constr_default = np.zeros(numdim)
    constr_default[i] = -1
    constr_a.append(constr_default)

constr_a = np.asarray(constr_a)
constr_b = np.arange(1, 2*numdim + 1, 1)
constr_b[numdim:] = constr_b[:numdim]

print linprog(np.transpose(arr1 * arr2), constr_a, constr_b, bounds=(None, None))

I get the following result:

fun: -4327476.2887400016 message: 'Optimization failed. The problem appears to be unbounded.' status: 3

I've tried changing the last row to:

print linprog(np.transpose(arr1 * arr2), constr_a, constr_b, bounds=(-1000, 1000))

The numbers specified as bounds are random. The output is:

fun: -4327476.2887400296 message: 'Optimization terminated successfully.' status: 0

which gives us a slightly different result and the desired status. My question is, do I misuse the library and in which way? Which answer is correct? This code was expected to work without specifying the 'bounds' parameter. I cannot use this parameter because these simple constraints are unique for each variable.

I use python 2.7 and scipy 0.17.1. Big thanks in advance.

Upd

constr_a should be a matrix according to the documentation (https://docs.scipy.org/doc/scipy/reference/optimize.linprog-simplex.html) and actually is in the code. To be sure the syntax is correct, we can cut the number of dimensions to 2:

from scipy.optimize import linprog
import numpy as np
import pandas as pd

numdim = 2

arr1 = np.ones(numdim)
arr1 = - arr1

arr2 = np.array([
19.53,
128.97
])

constr_a = []

for i in range(numdim):

    constr_default = np.zeros(numdim)
    constr_default[i] = 1
    constr_a.append(constr_default)

for i in range(numdim):

    constr_default = np.zeros(numdim)
    constr_default[i] = -1
    constr_a.append(constr_default)

constr_a = np.asarray(constr_a)
constr_b = np.arange(1, 2*numdim + 1, 1)
constr_b[numdim:] = constr_b[:numdim]

print constr_a
print constr_b
print linprog(np.transpose(arr1 * arr2), constr_a, constr_b, bounds=(None, None))

and this will work.

Upvotes: 0

Views: 469

Answers (1)

tihom
tihom

Reputation: 8003

the constr_a list is not properly formed. It is an array of array's instead of being an array of scalar. This might be leading to a improper lower bound causing the optimization to fail.

Perhaps 

constr_a.append(constr_default)

should be 

constr_a.append(constr_default[i])

inspect both the bound arrays to make sure they have proper form and values.

Upvotes: 0

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