HDU 1532 网络流裸题

本文详细介绍了一个标准的网络流算法实现,通过具体的代码示例讲解了如何进行最大流的计算,并针对一个具体问题进行了求解。文章包括了初始化图、广度优先搜索(BFS)寻找增广路径以及调整流量等关键步骤。

转自:https://blog.youkuaiyun.com/qq_38987374/article/details/80083754

题目:http://acm.hdu.edu.cn/showproblem.php?pid=1532

标准的网络流裸题

#include<iostream>
#include<cstdio>
#include<cstring>
#include<algorithm>
#include<cmath>
#include<queue>
#define LL long long
#define inf 10000010
using namespace std;
queue<int> q;
int c[210][210],f[210],pre[210];
int n,m;
int bfs(int src,int des)
{
    while(!q.empty())
        q.pop();
    memset(pre,-1,sizeof pre);
    f[src]=inf;
    q.push(src);
    int now;
    while(!q.empty())
    {
        now=q.front();
        q.pop();
        if(now==des)
            break;
        for(int next=1; next<=m; next++)
        {
            if(next==src||c[now][next]<=0||pre[next]!=-1)
                continue;
            pre[next]=now;
            f[next]=min(f[now],c[now][next]);
            q.push(next);
        }
    }
    if(pre[des]==-1)      return -1;
    else    return f[des];
}
int maxflow(int src,int des)
{
    int cnt,ans=0;
    int front,now;
    while((cnt=bfs(src,des))!=-1)
    {
        front=pre[des];
        now=des;
        while(front!=-1)
        {
            c[front][now]-=cnt;
            c[now][front]+=cnt;
            now=front;
            front=pre[now];
        }
        ans+=cnt;
    }
    return ans;
}
int main()
{
    while(scanf("%d%d",&n,&m)!=EOF)
    {
        memset(c,0,sizeof c);
        int u,v,val;
        for(int i=1; i<=n; i++)
        {
            scanf("%d%d%d",&u,&v,&val);
            if(u==v)
                continue;
            else
                c[u][v]+=val;
        }
        cout<<maxflow(1,m)<<endl;
    }
    return 0;
}

 

### 关于网络流算法的模板网络流是一种经典的图论算法,主要用于解决最大流、最小割等问。以下是几个常见的网络流算法模板及其对应的经典目。 #### 1. 最大流问 最大流问网络流中最基本的问之一,通常可以通过 **Edmonds-Karp 算法** 或者 **Dinic 算法** 解决。下面是一个简单的 Edmonds-Karp 算法实现: ```python from collections import deque class Edge: def __init__(self, v, flow, rev): self.v = v self.flow = flow self.rev = rev def add_edge(u, v, capacity, graph): edge_u_to_v = Edge(v, capacity, len(graph[v])) edge_v_to_u = Edge(u, 0, len(graph[u])) graph[u].append(edge_u_to_v) graph[v].append(edge_v_to_u) def bfs(s, t, parent, graph): visited = [False] * len(graph) queue = deque([s]) visited[s] = True while queue: u = queue.popleft() for idx, edge in enumerate(graph[u]): if not visited[edge.v] and edge.flow > 0: queue.append(edge.v) visited[edge.v] = True parent[edge.v] = u if edge.v == t: return True return False def edmonds_karp(n, s, t, graph): parent = [-1] * n max_flow = 0 while bfs(s, t, parent, graph): path_flow = float('Inf') v = t while v != s: u = parent[v] path_flow = min(path_flow, graph[u][next(i for i, e in enumerate(graph[u]) if e.v == v)].flow) v = u v = t while v != s: u = parent[v] index = next(i for i, e in enumerate(graph[u]) if e.v == v) graph[u][index].flow -= path_flow graph[v][graph[u][index].rev].flow += path_flow v = u max_flow += path_flow return max_flow ``` 上述代码实现了基于 BFS 的 Edmonds-Karp 算法来求解最大流问[^4]。 --- #### 2. 最小费用最大流问 如果需要考虑每条边的成本,则可以使用 **SPFA** 或 **Bellman-Ford** 结合最短路径的思想来计算最小费用的最大流。以下是一个 SPFA 实现的例子: ```python import heapq INF = int(1e9) def spfa_min_cost_max_flow(n, edges, start, end): adj_list = [[] for _ in range(n)] residual_graph = [[None]*n for _ in range(n)] for u, v, cap, cost in edges: adj_list[u].append((v, cap, cost)) adj_list[v].append((u, 0, -cost)) dist = [INF] * n potential = [0] * n prev_node = [-1] * n prev_edge = [-1] * n total_flow = 0 total_cost = 0 while True: pq = [] dist[start] = 0 heapq.heappush(pq, (dist[start], start)) while pq: d, node = heapq.heappop(pq) if d > dist[node]: continue for idx, (neighbor, cap, cost) in enumerate(adj_list[node]): if cap > 0 and dist[neighbor] > dist[node] + cost + potential[node] - potential[neighbor]: dist[neighbor] = dist[node] + cost + potential[node] - potential[neighbor] prev_node[neighbor] = node prev_edge[neighbor] = idx heapq.heappush(pq, (dist[neighbor], neighbor)) if dist[end] == INF: break for i in range(n): potential[i] += dist[i] flow = INF cur = end while cur != start: previous = prev_node[cur] edge_index = prev_edge[cur] flow = min(flow, adj_list[previous][edge_index][1]) cur = previous total_flow += flow total_cost += flow * potential[end] cur = end while cur != start: previous = prev_node[cur] edge_index = prev_edge[cur] adj_list[previous][edge_index] = ( adj_list[previous][edge_index][0], adj_list[previous][edge_index][1] - flow, adj_list[previous][edge_index][2] ) back_edge_index = None for j, (back_neighbor, _, _) in enumerate(adj_list[cur]): if back_neighbor == previous: back_edge_index = j break adj_list[cur][back_edge_index] = ( adj_list[cur][back_edge_index][0], adj_list[cur][back_edge_index][1] + flow, adj_list[cur][back_edge_index][2] ) cur = previous return total_flow, total_cost ``` 该代码通过调整势能函数优化了 SPFA,在处理负权边时更加高效[^5]。 --- #### 3. 经典模板推荐 以下是几道经典的网络流算法模板,适合初学者练习: 1. **POJ 1273 Drainage Ditches**: 这是一道典型的 Edmonds-Karp 算法入门。 2. **HDU 3549 Flow Problem**: 需要使用 Dinic 算法提高效率。 3. **Codeforces Round #XXX Div.2 C**: 涉及到最小费用最大流的应用场景。 4. **LeetCode 787 Cheapest Flights Within K Stops**: 虽然不是纯网络流,但可以用类似思路建模。 ---
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