Why am I not getting the values ​I want with scipy.optimize. (linear programming problems)

Question

import numpy as np
from scipy.optimize import linprog

b_ub = [74, 40, 36]
b_eq = [20, 45, 30]
A = np.array([[7, 3, 6], [4, 8, 2], [1, 5, 9]])
m, n = A.shape
c = list(np.reshape(A, n * m))  # Convert matrix A to list c.
A_ub = np.zeros([m, m * n])
for i in np.arange(0, m,
                   1):  # Filling in the matrix of conditions-inequalities.
    for j in np.arange(0, n * m, 1):
        if i * n <= j <= n + i * n - 1:
            A_ub[i, j] = 1
A_eq = np.zeros([m, m * n])
for i in np.arange(0, m, 1):  # Filling in the matrix of conditions-equalities
    k = 0
    for j in np.arange(0, n * m, 1):
        if j == k * n + i:
            A_eq[i, j] = 1
            k = k + 1
print(linprog(c, A_ub, b_ub, A_eq, b_eq))

I'm getting this

instead of

Answer 1

I believe it is due to the default solver. Swapping the solver gave the solution you looked for. See the docs and evaluate the tradeoffs of using each solver to determine what you want to use.

print(linprog(c, A_ub, b_ub, A_eq, b_eq, method='highs-ds'))

           con: array([0., 0., 0.])
 crossover_nit: 0
           fun: 215.0
       message: 'Optimization terminated successfully.'
           nit: 3
         slack: array([29., 10., 16.])
        status: 0
       success: True
             x: array([ 0., 45.,  0.,  0.,  0., 30., 20.,  0.,  0.])