如何使用scipy.optimize.linprog获得整数解？

import pulp
from mip import Model, xsum, maximize, BINARY

# 创建一个模型
model = Model()

# 定义决策变量
x = [model.add_var(var_type=BINARY) for _ in range(4)]

# 定义目标函数
model.objective = maximize(xsum([x[i] for i in range(4)]))

# 定义约束条件
model += xsum([x[i] for i in range(4)]) == 1
model += x[0] + x[1] <= 1
model += x[2] + x[3] <= 1

# 求解模型
model.optimize()

# 输出结果
for v in model.vars:
    print(v.name, "=", v.x)

import cvxpy as cp
from mip import Model, xsum, maximize, BINARY

# 创建一个模型
model = Model()

# 定义决策变量
x = [model.add_var(var_type=BINARY) for _ in range(4)]

# 定义目标函数
objective = xsum([x[i] for i in range(4)])

# 定义约束条件
constraints = [
    xsum([x[i] for i in range(4)]) == 1,
    x[0] + x[1] <= 1,
    x[2] + x[3] <= 1
]

# 求解模型
model.objective = maximize(objective)
for constraint in constraints:
    model += constraint

model.optimize()

# 输出结果
for v in model.vars:
    print(v.name, "=", v.x)