POJ 1273 Drainage Ditches 【费用流】

本文深入探讨了最大流算法的原理与应用,通过解决一个典型问题——如何在一个有向图中找到从起点到终点的最大流量,介绍了最大流算法的基本思想和实现过程。文章提供了详细的代码实现,并附有清晰的注释,帮助读者更好地理解和掌握最大流算法。

题面:

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

题目大意:

有一个有向图有n条路,m个点。每一条路都有最大承载能力。

问最多能从第一个点传递多少东西到最后一个点

大致思路:

最大流模板题,通过这个题可以较好的理解最大流算法和实现过程

关于最大流的思想参见kuangbin神犇的博客:http://www.cnblogs.com/kuangbin/archive/2011/07/26/2117636.html

代码:

#include<iostream>
#include<cstdlib>
#include<queue>
#include<algorithm>
#include<cstring>
#include<cstdio>
using namespace std;
const int maxn=250;
const int INF=1<<29;
int n,m,start,tar;
int g[maxn][maxn];
int path[maxn],flow[maxn];
queue<int> q;
int bfs()
{
    int u;
    while(!q.empty()) q.pop();//记得将队列清空
    memset(path,-1,sizeof(path));
    path[start]=0;
    flow[start]=INF;
    q.push(start);
    while(!q.empty())
    {
        u=q.front();
        q.pop();
        if(u==tar)
            break;
        for(int i=1;i<=m;++i){
            if(i!=start && path[i]==-1 && g[u][i]){
                //flow[i]= flow[u]<g[u][i]?flow[u]:g[u][i];
                flow[i]=min(flow[u],g[u][i]);
                q.push(i);
                path[i]=u;
            }
        }
    }
    if(path[tar]==-1)//若path[tar]未更新,则代表增广路径不存在
        return -1;
    return flow[tar];
}
int Edmonds_Karp()
{
    int max_flow=0,step,now,pre;
    while(1)
    {
        step=bfs();//每次寻找是否还有增广路径
        if(step==-1)
            break;
        max_flow+=step;
        now=tar;
        while(now!=start)
        {
            pre=path[now];
            g[pre][now]-=step;
            g[now][pre]+=step;//增加反向边
            now=pre;
        }
    }
    return max_flow;
}
int main()
{
    ios::sync_with_stdio(false);
    int u,v,l;
    //freopen("in.txt","r",stdin);
    while(cin>>n>>m)
    {
        memset(g,0,sizeof(g));
        for(int i=0;i<n;++i){
            cin>>u>>v>>l;
            g[u][v]+=l;
        }
        start=1;
        tar=m;
        cout<<Edmonds_Karp()<<endl;
    }

    return 0;
}

 

转载于:https://www.cnblogs.com/SCaryon/p/7517472.html

内容概要:本文介绍了一个基于Google Earth Engine(GEE)平台的JavaScript函数库,主要用于时间序列数据的优化与子采样处理。核心函数包括de_optim,采用差分进化算法对时间序列模型进行参数优化,支持自定义目标函数、变量边界及多种变异策略,并可返回最优参数或收敛过程的“陡度图”(scree image);sub_sample函数则用于按时间密度对影像集合进行三种方式的子采样(批量、分段打乱、跳跃式),以减少数据量同时保留时序特征;配套函数ts_image_to_coll可将子采样后的数组图像还原为标准影像集合,apply_model可用于将优化所得模型应用于原始时间序列生成预测结果。整个工具链适用于遥感时间序列建模前的数据预处理与参数调优。; 适合人群:具备Earth Engine基础开发经验、熟悉JavaScript语法并从事遥感数据分析、生态建模等相关领域的科研人员或技术人员;有时间序列建模需求且希望自动化参数优化程的用户。; 使用场景及目标:①在有限观测条件下优化非线性时间序列拟合模型(如物候模型)的参数;②压缩大规模时间序列数据集以提升计算效率;③实现模型验证与交叉验证所需的时间序列子集抽样;④构建端到端的遥感时间序列分析水线。; 阅读建议:此资源为功能性代码模块,建议结合具体应用场景在GEE平台上实际调试运行,重点关注各函数的输入格式要求(如band命名、image属性设置)和异常处理机制,确保输入数据符合规范以避免运行错误。
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