请修改一下代码,在求解完成后输出最优解目标函数的最大值。代码:import pulp def integer_programming_solver(n): # 创建模型 model = pulp.LpProblem("integer_programming_solver", pulp.LpMaximize) # 创建n个整数型变量,取值为0或1 x = pulp.LpVariable.dicts("x", range(n), lowBound=0, upBound=1, cat=pulp.LpInteger) # 添加约束条件 model += sum(x[i] for i in range(n)) == 1 model += sum(x[i] for i in range(n)) <= n / 3 # 设置目标函数 model += sum(x[i] for i in range(n)) # 求解模型 status = model.solve() # 输出结果 if status == pulp.LpStatusOptimal: print("最优解为:") for i in range(n): print(f"x[{i}] = {int(pulp.value(x[i]))}") else: print("无法找到最优解")
时间: 2024-02-15 09:02:56 浏览: 24
可以在求解完成后,使用 `pulp.value()` 函数获取最优解目标函数的最大值,并在输出时打印出来。修改后的代码如下:
```python
import pulp
def integer_programming_solver(n):
# 创建模型
model = pulp.LpProblem("integer_programming_solver", pulp.LpMaximize)
# 创建n个整数型变量,取值为0或1
x = pulp.LpVariable.dicts("x", range(n), lowBound=0, upBound=1, cat=pulp.LpInteger)
# 添加约束条件
model += sum(x[i] for i in range(n)) == 1
model += sum(x[i] for i in range(n)) <= n / 3
# 设置目标函数
model += sum(x[i] for i in range(n))
# 求解模型
status = model.solve()
# 输出结果
if status == pulp.LpStatusOptimal:
print("最优解为:")
for i in range(n):
print(f"x[{i}] = {int(pulp.value(x[i]))}")
print(f"最大值为:{pulp.value(model.objective)}")
else:
print("无法找到最优解")
```
其中,在输出结果时,使用 `pulp.value(model.objective)` 获取目标函数的最大值,并打印出来。