scipyinimize 函数中多限制条件的问题

Question

par_0 = np.array([5, 0.1, 0.1])
w = par_0[0:2].reshape((-1,1))
b = par_0[-1]


def primal_L(params):
    w = params[0:2].reshape((-1,1))
    return 0.5 * np.dot(w.T, w)


cons_temp = []
for i in range(y_train.shape[0]):
    cons_temp.append({
        'type' : 'ineq',
        'fun': lambda x: y_train[i] * (np.dot(X_test[i], x[0:2].reshape((-1, 1))) + x[-1]) - 1,
        'args': ()
    })            #这里添加了300个限制条件，但是args后面应该填什么呢


res = minimize(primal_L, par_0, constraints=cons)
print(res)

Answer 1

[ a>1 , 1<b<2 , 1< k < b, ... ]  三百个条件，直接用all() 函数判断