将常量和变量传递到Scipy.minimize.optimize约束和边界

Question

我发现如何将*args传递到scipy.minimize.optimize的主要功能中，请参阅this question。

但是如何将常量和变量传递到约束和边界中？

P是变量向量，P0是初始解向量，f(P)是最小化函数，其余都是常数数据。会这样吗？

def constraint_1(P, args):
    M_goal, other_args = args
    M = calc_M_from_P(P, args)  # calculate M from P
    return M-M_goal

# boundary is simply (P0[i]-500, P0[i]+500)
def get_boundaries(P0): 
    list_bnds = []
    for i in range(len(P0)):
        list_bnds.append((P0[i]-500.0, P0[i]+500.0))
    return tuple(list_bnds)

func = objective(P0, args)
bnds = get_boundaries(P0)
con1 = {'type': 'ineq', 'fun': constraint_1}

sol = minimize(func, P0, args = args, method='SLSQP', bound=bnds, constraints = con1)