调用pulp库来重写以上代码

好的，以下是使用pulp库重新编写的代码：

import pulp as lp
import numpy as np


def integer_cutting_plane(c, A, b, bounds):
    relaxed_A = A
    relaxed_b = b
    while True:
        lp_model = lp.LpProblem("Integer Cutting Plane", lp.LpMinimize)

        # 定义决策变量
        x = [lp.LpVariable(f"x{i}", lowBound=bound[0], upBound=bound[1], cat='Integer') for i, bound in enumerate(bounds)]

        # 定义目标函数
        lp_model += sum(c[i] * x[i] for i in range(len(c)))

        # 添加松弛约束
        for i in range(len(relaxed_A)):
            lp_model += sum(relaxed_A[i][j] * x[j] for j in range(len(x))) <= relaxed_b[i]

        lp_model.solve()

        if lp_model.status == lp.LpStatusOptimal:
            return np.array([int(x[i].varValue) for i in range(len(x))])

        new_constraint = [relaxed_A[i] @ [x[j].varValue for j in range(len(x))] <= relaxed_b[i] for i in range(len(relaxed_A))]
        relaxed_A = np.vstack((relaxed_A, new_constraint))
        relaxed_b = np.append(relaxed_b, np.inf)


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()
    relaxed_A = get_A()
    relaxed_b = get_b()
    integer_cutting_plane(get_c(), relaxed_A, relaxed_b, bounds)

在这个代码中，我们使用pulp库来定义和解决整数线性规划问题。我们首先创建一个线性规划模型`lp_model`，然后定义决策变量、目标函数和约束条件。在每次迭代中，我们调用`lp_model.solve()`来求解整数线性规划问题，并根据求解结果判断是否找到了整数可行解。

希望这次的代码能够满足你的需求。如果有任何进一步的问题，请随时提问。