scipy.optimize.linprog non è in grado di trovare un punto di partenza fattibile nonostante una risposta possibile esista chiaramente

Question

il vettore k sembra soddisfare tutti i vincoli. C'è qualcosa che mi manca qui? Grazie.

import numpy as np 
from scipy.optimize import linprog 
A_ub=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1, 0, 0, 1, -1, 0, 0, 1, -1, 0, 0, 1, -1, 0, 0, 1, -1, 0, 0, 1, -1, 0, 0, 1, -1, 0, 0, 1, -1, 0, 0, 1, -1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, -1, 1, 0, 0, -1, 1, 0, 0, -1, 1, 0, 0, -1, 1, 0, 0, -1, 1, 0, 0, -1, 1, 0, 0, -1, 1, 0, 0, -1, 1]] 
b_ub=[ 10000., 100., 10000., 1840., 10000., 4000., 10000., 
     100., 10000., 5000., 10000., 5450., 10000., 3000., 
    10000., 3000., 10000., 1000., 40000., 5000., 5000.] 
A_eq=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, -5000.0, 13390.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, -1840.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 5000.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [13290.0, 0.0, -13390.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, -5450.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, -1.0, 1.0, -1.0, 1.0, 1.0, 1.0, -1.0, 1.0, -1.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 1, -1, 0, 0, 0, 0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1, 1, -1], [-13290.0, 5000.0, 0.0, 1840.0, 5450.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 0.0, -1.0, -1.0, 0.0, -1.0, -1.0, 0.0, 0.0, 1.0, -1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 1]] 
b_eq=[8390.0, 0, -1840.0, 0, 5000.0, 0, -100.0, 0, 0.0, 0, -5450.0, 0, 0.0, 0, 0.0, 0, -1000.0, 0] 
c=[-1351.6146468256165, -99.629272305631787, -542.0389808700279, -0.0, 54.302887927385768, -0.043903442258601377, -0.10170162880553937, -0.043903442258601377, -0.043903442258601377, -0.043903442258601377, -0.11764394156352764, -0.043903442258601377, -0.056182133775492554, -0.043903442258601377, -0.043903442258601377, -0.033174575907634424, -0.081074546005202835, -0.043903442258601377, -0.057503980336247616, -0.040480879825992883, -0.046889515046147204, -0.10170162880553937, 0.02, 0.02, 0, 0, 0.02, 0.02, 0, 0, 0.02, 0.02, 0, 0, 0.02, 0.02, 0, 0, 0.02, 0.02, 0, 0, 0.02, 0.02, 0, 0, 0.02, 0.02, 0, 0, 0.02, 0.02, 0, 0, 0.02, 0.02, 0, 0] 
k=[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8390.0, 0, 0, 0, 0, 1840.0, 0, 0, 5000.0, 0, 0, 0, 0, 100.0, 0, 0, 0.0, 0.0, 0, 0, 0, 5450.0, 0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0, 0, 1000.0] 

print(all(np.dot(A_ub, k)<=b_ub)) 
print(all(np.dot(A_eq, k)==b_eq)) 
print(all(map(lambda x :x>=0, k))) 
linprog(c, A_ub, b_ub, A_eq, b_eq) 

Answer 1

Questa risposta non spiega il motivo per cui funziona. Spero che qualcuno più familiare con il codice linprog o con la programmazione lineare in generale possa dare una risposta più approfondita.

---

ottengo una soluzione, se si utilizza l'opzione bland=True (vedi show_options per la documentazione - scorrere verso il basso per le linprog opzioni):

In [130]: linprog(c, A_ub, b_ub, A_eq, b_eq, options=dict(bland=True)) 
Out[130]: 
    status: 0 
    slack: array([ 3610., 6490., 11840.,  0.,  0., 14000., 10100., 
      0., 10000., 5000., 15450.,  0., 13000.,  0., 
     10000., 3000., 11000.,  0., 12220.,  0., 10000.]) 
success: True 
    fun: -2683.6935269049131 
     x: array([ 1.22573363e+00, 2.00000000e+00, 1.22404780e+00, 
     3.71739130e+00, 8.25688073e-02, 2.00000000e+03, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     5.00000000e+03, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 2.00000000e+03, 
     6.39000000e+03, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 1.84000000e+03, 
     5.00000000e+03, 0.00000000e+00, 1.00000000e+04, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 1.00000000e+02, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, -1.11022302e-12, 0.00000000e+00, 
     5.45000000e+03, 0.00000000e+00, 3.00000000e+03, 
     0.00000000e+00, 3.00000000e+03, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     1.00000000e+03]) 
message: 'Optimization terminated successfully.' 
    nit: 50 

Un componente è leggermente negativo (-1.11e -12). Presumibilmente, questo è all'interno della tolleranza predefinita. Che possono essere puliti fino abbassando la tolleranza (ma si noti la variazione x[19]):

In [131]: linprog(c, A_ub, b_ub, A_eq, b_eq, options=dict(bland=True, tol=1e-15)) 
Out[131]: 
    status: 0 
    slack: array([ 3610., 6490., 11840.,  0.,  0., 14000., 10100., 
      0., 10000., 5000., 15450.,  0., 13000.,  0., 
     10000., 3000., 11000.,  0., 12220.,  0., 10000.]) 
success: True 
    fun: -2683.693526904935 
     x: array([ 1.22573363e+00, 2.00000000e+00, 0.00000000e+00, 
     3.71739130e+00, 8.25688073e-02, 2.00000000e+03, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     5.00000000e+03, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 1.63900000e+04, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 2.00000000e+03, 
     6.39000000e+03, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 1.84000000e+03, 
     5.00000000e+03, 0.00000000e+00, 1.00000000e+04, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 1.00000000e+02, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     5.45000000e+03, 0.00000000e+00, 3.00000000e+03, 
     0.00000000e+00, 3.00000000e+03, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     1.00000000e+03]) 
message: 'Optimization terminated successfully.' 
    nit: 51 

Answer 2

Sembra un problema di tolleranza.

sono stato in grado di "fissare" è importando il original linprog code, dopo ho cambiato la tolleranza (tol parametri) da 10e-12 al 10e-8 nel metodo "privato" _linprog_simplex.

Questo parametro viene passato al metodo _pivot_col, in cui si legge

ma = np.ma.masked_where(T[-1, :-1] >= -tol, T[-1, :-1], copy=False) 
    if ma.count() == 0: 
     return False, np.nan 
    if bland: 
     return True, np.where(ma.mask == False)[0][0] 
    return True, np.ma.where(ma == ma.min())[0][0] 

Questo è il motivo per cui bland s' regola supera il test, mentre quello di default non riesce. Ho quindi provato a trovare se esiste una tolleranza di default nell'implementazione di numpy.masked_where. Da lì, non è evidente quale sia la tolleranza utilizzata, ma altre funzioni di numpy, come ad esempio masked_values, hanno una tolleranza assoluta di 10e-8 per impostazione predefinita.

Spero che questo aiuti.

Ecco il risultato che sto ottenendo cambiando la tolleranza _linprog_simplex:

True 
True 
True 
    status: 0 
    slack: array([ 3610., 6490., 11840.,  0.,  0., 14000., 10100., 
      0., 10000., 5000., 15450.,  0., 13000.,  0., 
     10000., 3000., 11000.,  0., 12220.,  0., 10000.]) 
success: True 
    fun: -2683.6935269049141 
     x: array([ 1.22573363e+00, 2.00000000e+00, 1.22404780e+00, 
     3.71739130e+00, 8.25688073e-02, 2.00000000e+03, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     5.00000000e+03, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 2.00000000e+03, 
     6.39000000e+03, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 1.84000000e+03, 
     5.00000000e+03, 0.00000000e+00, 1.00000000e+04, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 1.00000000e+02, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     5.45000000e+03, 0.00000000e+00, 3.00000000e+03, 
     0.00000000e+00, 3.00000000e+03, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 
     1.00000000e+03]) 
message: 'Optimization terminated successfully.' 
    nit: 26 

PS: ho anche dovuto modificare la riga

from .optimize import OptimizeResult, _check_unknown_options 

a

from scipy.optimize import OptimizeResult 

e rimuovere la chiamata a _check_unknown_options nella riga 533 del codice originale.