BNUOJ 1392 Drainage Ditches

本文提供了一个使用Dinic算法解决最大流问题的C++代码模板。该算法通过不断寻找增广路径来更新流量矩阵,实现从源点到汇点的最大流量计算。

和前面那个POJ 1273是一样的,这里用的是Dinic算法,所以当做一个模板保存一下。

#include <iostream>
#include <cstdio>
#include <cstring>
#include <queue>
#define INF 0x7fffffff
using namespace std;
int val[300][300];
int lev[300];
int n,m;
bool bfs()
{
    int u,v;
    memset(lev,-1,sizeof(lev));
    lev[1]=0;
    queue<int> q;
    while (!q.empty())
        q.pop();
    q.push(1);
    while (!q.empty())
    {
        u=q.front();
        q.pop();
        for (v=1; v<=n; v++)
        {
            if (val[u][v] > 0 && lev[v] < 0)
            {
                lev[v]=lev[u]+1;
                q.push(v);
            }
        }
    }
    return lev[n] != -1;
}
int dfs(int u,int flow)
{
    int v,t;
    if (u == n)
        return flow;
    for (v=1; v<=n; v++)
    {
        if (val[u][v] != 0 && lev[v] == lev[u]+1 && (t=dfs(v,min(flow,val[u][v]))))
        {
            val[u][v]-=t;
            val[v][u]+=t;
            return t;
        }
    }
    return 0;
}
int dinic()
{
    int ans,t;
    ans=0;
    while(bfs() == true)
    {
        while(1)
        {
            t=dfs(1,INF);
            if (t == 0)
                break;
            ans+=t;
        }
    }
    return ans;
}
int main()
{
    int t1,t2,t3,i,j;
    while (scanf("%d%d",&m,&n) != EOF)
    {
        memset(val,0,sizeof(val));
        for (i=0; i<m; i++)
        {
            scanf("%d%d%d",&t1,&t2,&t3);
            val[t1][t2]+=t3;
        }
        printf("%d\n",dinic());
    }
}


Description Every time it rains on Farmer John's fields, a pond forms over Bessie's favorite clover patch. This means that the clover is covered by water for awhile and takes quite a long time to regrow. Thus, Farmer John has built a set of drainage ditches so that Bessie's clover patch is never covered in water. Instead, the water is drained to a nearby stream. Being an ace engineer, Farmer John has also installed regulators at the beginning of each ditch, so he can control at what rate water flows into that ditch. Farmer John knows not only how many gallons of water each ditch can transport per minute but also the exact layout of the ditches, which feed out of the pond and into each other and stream in a potentially complex network. Given all this information, determine the maximum rate at which water can be transported out of the pond and into the stream. For any given ditch, water flows in only one direction, but there might be a way that water can flow in a circle. Input The input includes several cases. For each case, the first line contains two space-separated integers, N (0 <= N <= 200) and M (2 <= M <= 200). N is the number of ditches that Farmer John has dug. M is the number of intersections points for those ditches. Intersection 1 is the pond. Intersection point M is the stream. Each of the following N lines contains three integers, Si, Ei, and Ci. Si and Ei (1 <= Si, Ei <= M) designate the intersections between which this ditch flows. Water will flow through this ditch from Si to Ei. Ci (0 <= Ci <= 10,000,000) is the maximum rate at which water will flow through the ditch. Output For each case, output a single integer, the maximum rate at which water may emptied from the pond. Sample Input 5 4 1 2 40 1 4 20 2 4 20 2 3 30 3 4 10 Sample Output 50
最新发布
08-21
This problem is a classic **maximum flow problem** in a directed graph. We are given a network of drainage ditches, where each ditch has a maximum capacity. The goal is to determine the maximum amount of water that can flow from the **source node (pond, node 1)** to the **sink node (stream, node M)**. To solve this efficiently, we can use the **Edmonds-Karp algorithm**, which is an implementation of the **Ford-Fulkerson method** using BFS to find augmenting paths. Here's a Python implementation using BFS for the Edmonds-Karp algorithm: ```python from collections import deque def bfs(capacity, flow, source, sink, parent, M): visited = [False] * (M + 1) queue = deque() queue.append(source) visited[source] = True while queue: u = queue.popleft() for v in range(1, M + 1): if not visited[v] and capacity[u][v] - flow[u][v] > 0: visited[v] = True parent[v] = u if v == sink: return True queue.append(v) return False def edmonds_karp(capacity, source, sink, M): flow = [[0] * (M + 1) for _ in range(M + 1)] parent = [-1] * (M + 1) max_flow = 0 while bfs(capacity, flow, source, sink, parent, M): path_flow = float('inf') s = sink while s != source: path_flow = min(path_flow, capacity[parent[s]][s] - flow[parent[s]][s]) s = parent[s] max_flow += path_flow v = sink while v != source: u = parent[v] flow[u][v] += path_flow flow[v][u] -= path_flow v = parent[v] return max_flow def main(): import sys input = sys.stdin.read data = input().split() index = 0 results = [] while index < len(data): N = int(data[index]) M = int(data[index + 1]) index += 2 capacity = [[0] * (M + 1) for _ in range(M + 1)] for _ in range(N): Si = int(data[index]) Ei = int(data[index + 1]) Ci = int(data[index + 2]) index += 3 capacity[Si][Ei] += Ci max_flow = edmonds_karp(capacity, 1, M, M) results.append(max_flow) for result in results: print(result) if __name__ == "__main__": main() ``` ### Explanation: - **`bfs()`**: This function checks if there's an augmenting path from the source to the sink and updates the parent array to track the path. - **`edmonds_karp()`**: The main function that repeatedly finds augmenting paths using BFS and increases the flow until no more augmenting paths exist. - **`main()`**: Reads the input, constructs the capacity matrix, runs the algorithm, and prints the result. ### Sample Input Execution: For the input: ``` 5 4 1 2 40 1 4 20 2 4 20 2 3 30 3 4 10 ``` The output will be: ``` 50 ``` This is because the max flow from node 1 to node 4 is 50, which is the maximum rate water can be drained. ---
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