以上整数解超出了b的限定范围
时间: 2024-04-17 22:24:54 浏览: 16
非常抱歉给你带来了困惑。我检查了之前的代码,发现在添加新的松弛约束时,没有考虑到限制条件 `b` 的范围。为了确保解满足这些限制,我们需要在添加新约束之前将其修正。
以下是修正后的代码:
```python
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)):
constraint = lp.LpConstraint(
e=lp.LpAffineExpression([(x[j], relaxed_A[i][j]) for j in range(len(x))]),
sense=lp.LpConstraintLE,
rhs=relaxed_b[i]
)
lp_model.addConstraint(constraint)
lp_model.solve()
if lp_model.status == lp.LpStatusOptimal:
return np.array([int(x[i].varValue) for i in range(len(x))])
new_constraint = [np.floor(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)
```
在修正后的代码中,我使用了 `lp.LpConstraint` 类来定义松弛约束,并确保在添加新约束之前,将其修正为向下取整的形式。这样,我们可以确保新约束满足限制条件 `b`。
非常抱歉之前的代码没有正确处理这个问题。希望这次的修正能够解决你的问题。如果还有其他疑问,请随时提问。