用分支限界法求 tsp，给出代码

分支限界法（Branch and Bound，简称BB算法）是一种用于解决组合优化问题如旅行商问题（Travelling Salesman Problem，TSP）的有效搜索策略。TSP的目标是最短路径，使得旅行商访问每个城市恰好一次并返回起点。

以下是使用Python实现一个简单版本的分支限界算法解TSP的伪代码示例：

# 假设我们有一个包含城市距离的邻接矩阵distances
def branch_and_bound(distances, current_city, visited_cities, best_path=None):
    # 初始化最低成本（上界）
    if not best_path or total_cost(best_path) > total_cost(visited_cities + [current_city]):
        best_path = visited_cities + [current_city]

    unvisited_cities = list(set(range(len(distances))) - set(visited_cities))
    
    # 枝节点：尝试所有未访问城市的下一个访问点
    for next_city in unvisited_cities:
        new_visited_cities = visited_cities.copy()
        new_visited_cities.append(next_city)
        
        # 检查当前扩展是否优于已知最优解
        if total_cost(new_visited_cities) < total_cost(best_path):
            branch_and_bound(distances, next_city, new_visited_cities, best_path)

    # 回溯过程中更新最佳解
    if total_cost(best_path) < total_cost(current_best):
        current_best = best_path

# 辅助函数计算路径总成本
def total_cost(path):
    cost = 0
    for i in range(len(path) - 1):
        cost += distances[path[i]][path[i+1]]
    return cost

# 示例调用
start_city = 0
branch_and_bound(distances, start_city, [], best_path=start_city)