Минимизация функции с LinearConstraint

Решаю задачу поиска минимума функции с помощью scipy.optimize.LinearConstraint. Вот пример кода:

import numpy as np
from scipy.optimize import LinearConstraint, minimize

m = 35
n = 20
np.random.seed(1)
s0 = np.random.randn(m)
lamb0 = np.maximum(-s0, 0)
s0 = np.maximum(s0, 0)
x0 = np.random.randn(n)
A = np.random.randn(m, n)
b = A@x0 + s0
c = -A.T@lamb0

linear_constraint = LinearConstraint (A, [-np.inf], b)
def f_2(x):
    return np.dot(np.transpose(c), x)

x = np.ones(n)
minimize(f_2, x0=x, constraints=linear_constraint)

Не могу понять с чем связана ошибка:

IndexError                                Traceback (most recent call last)
<ipython-input-37-51d74c49b301> in <module>()
      1 x = np.ones(n)
----> 2 minimize(f_2, x0=x, constraints=linear_constraint)

3 frames
/usr/local/lib/python3.6/dist-packages/scipy/optimize/_constraints.py in __init__(self, constraint, x0, sparse_jacobian, finite_diff_bounds)
    242         mask = keep_feasible & (lb != ub)
    243         f0 = fun.f
--> 244         if np.any(f0[mask] < lb[mask]) or np.any(f0[mask] > ub[mask]):
    245             raise ValueError("`x0` is infeasible with respect to some "
    246                              "inequality constraint with `keep_feasible` "

IndexError: boolean index did not match indexed array along dimension 0; dimension is 1 but corresponding boolean dimension is 15

Буду признателен за помощь!