scipy.linprog可行性的Bug?(A_ub@x0<=b_ub).all()为True,但--linprog(np.zeros_like(x0),A_ub=A_ub,b_ub=b_ub

有numpy,scipy的版本

numpy 1.25.0
scipy 1.11.0

以下内容 scipy.optimize.linprog 呼叫

import numpy as np
from scipy.optimize import linprog

A_ub = np.array(
      [[-0.15729144,  0.29943807,  0.29311432],
       [-1.32475528, -2.1125364 , -1.55138585],
       [ 1.00861965,  0.53283629, -0.14939833],
       [ 1.07581479,  0.164022  , -1.19889684]])

b_ub = -np.ones(4)

print(linprog(np.zeros(3),
        A_ub=A_ub,
        b_ub=b_ub))

返回不可行状态,

       message: The problem is infeasible. (HiGHS Status 8: model_status is Infeasible; primal_status is At lower/fixed bound)
       success: False
        status: 2
           fun: None
             x: None
           nit: 0

但这个问题实际上是可行的,因为

x0 = np.array([ 229.1748166 , -507.05266751,  512.14005547])
print('x0 is feasible?', (A_ub @ x0 <= b_ub).all())

返回True。不应该 linprog 在这种情况下返回一个可行的点和不同的状态代码和消息?