import numpy as np from scipy.optimize import linprog def integer_cutting_plane(c, A, b, bounds): relaxed_A = A relaxed_b = b while True: res = linprog(c=c, A_ub=relaxed_A, b_ub=relaxed_b, bounds=bounds) x = res.x if all(int(val) == val for val in x): return x.astype(int) new_constraint = (relaxed_A @ x <= relaxed_b) relaxed_A = np.vstack((relaxed_A, new_constraint)) def get_bounds(): return [(0, None), (0, None)] def get_c(): return np.array([40, 90]) def get_A(): return np.array([[-9, -7], [-7, -20]]) def get_b(): return np.array([-56, -70]) if __name__ == '__main__': bounds = get_bounds() integer_cutting_plane(get_c(), get_A(), get_b(), bounds)以上代码运行报错ValueError: Invalid input for linprog: b_ub must be a 1-D array; b_ub must not have more than one non-singleton dimension and the number of rows in A_ub must equal the number of values in b_ub 请解决

在你的代码中，报错是因为在调用linprog函数时，传递给参数b_ub的参数relaxed_b是一个二维数组。linprog函数要求b_ub是一个一维数组。

要解决这个问题，你可以使用numpy库的flatten()方法将relaxed_b转换为一维数组。flatten()方法会将多维数组展平为一维数组。

以下是修改后的代码：

import numpy as np
from scipy.optimize import linprog


def integer_cutting_plane(c, A, b, bounds):
    relaxed_A = A
    relaxed_b = b
    while True:
        res = linprog(c=c, A_ub=relaxed_A, b_ub=relaxed_b.flatten(), bounds=bounds)
        x = res.x
        if all(int(val) == val for val in x):
            return x.astype(int)

        new_constraint = (relaxed_A @ x <= relaxed_b)
        relaxed_A = np.vstack((relaxed_A, new_constraint))


def get_bounds():
    return [(0, None), (0, None)]


def get_c():
    return np.array([40, 90])


def get_A():
    return np.array([[-9, -7], [-7, -20]])


def get_b():
    return np.array([-56, -70])


if __name__ == '__main__':
    bounds = get_bounds()
    integer_cutting_plane(get_c(), get_A(), get_b(), bounds)

通过使用flatten()方法将relaxed_b转换为一维数组，你可以解决报错问题。请注意，这里使用的是numpy库的flatten()方法，而不是reshape()方法。