B - Out of Hay POJ - 2395

帮助Bessie规划路线,使用Kruskal算法找出连接N个农场的最小生成树中最大边的长度,确保她携带的水量足够通过最长的道路。
The cows have run out of hay, a horrible event that must be remedied immediately. Bessie intends to visit the other farms to survey their hay situation. There are N (2 <= N <= 2,000) farms (numbered 1..N); Bessie starts at Farm 1. She'll traverse some or all of the M (1 <= M <= 10,000) two-way roads whose length does not exceed 1,000,000,000 that connect the farms. Some farms may be multiply connected with different length roads. All farms are connected one way or another to Farm 1. 

Bessie is trying to decide how large a waterskin she will need. She knows that she needs one ounce of water for each unit of length of a road. Since she can get more water at each farm, she's only concerned about the length of the longest road. Of course, she plans her route between farms such that she minimizes the amount of water she must carry. 

Help Bessie know the largest amount of water she will ever have to carry: what is the length of longest road she'll have to travel between any two farms, presuming she chooses routes that minimize that number? This means, of course, that she might backtrack over a road in order to minimize the length of the longest road she'll have to traverse.
Input
* Line 1: Two space-separated integers, N and M. 

* Lines 2..1+M: Line i+1 contains three space-separated integers, A_i, B_i, and L_i, describing a road from A_i to B_i of length L_i.
Output
* Line 1: A single integer that is the length of the longest road required to be traversed.
Sample Input
3 3
1 2 23
2 3 1000
1 3 43
Sample Output
43
题意,找到所有村庄中一条最长的路,用Kruskal算法,求最小生成树,刚开始的时候要把权值从小到大排序即可,然后求最大的一个权值,就是题中所要求的。
#include<stdio.h>
#include<string.h>
#include<algorithm>
using namespace std;
struct note
{
    int u;
    int v;
    int w;
} q[10010];
int f[2010];
int cmp(note a,note b)
{
    return a.w<b.w;
}
int find(int v)
{
    if(f[v]==v)return v;
    else return find(f[v]);
}
int merge(int x,int y)
{
    int t1,t2;
    t1=find(x);
    t2=find(y);
    if(t1!=t2)
    {
        f[t2]=t1;
        return 1;
    }
    return 0;
}
int main()
{
    int i,k,n,m;
    while(~scanf("%d%d",&n,&m))
    {
        for(i=0; i<m; i++)
            scanf("%d%d%d",&q[i].u,&q[i].v,&q[i].w);
        sort(q,q+m,cmp);   //按照权值从小到大排序
        for(i=1; i<=n; i++)
            f[i]=i;
        int count=0;
        for(i=0; i<m; i++)
        {
            if(merge(q[i].u,q[i].v))//并查集看是否被连通
            {
                count++;
                if(count==n-1)
                {
                    k=i;    //最长的一条路的下标
                    break;
                }
            }
        }
        printf("%d\n",q[k].w);
    }
}

【电动汽车充电站有序充电调度的分散式优化】基于蒙特卡诺和拉格朗日的电动汽车优化调度(分时电价调度)(Matlab代码实现)内容概要:本文介绍了基于蒙特卡洛和拉格朗日方法的电动汽车充电站有序充电调度优化方案,重点在于采用分散式优化策略应对分时电价机制下的充电需求管理。通过构建数学模型,结合不确定性因素如用户充电行为和电网负荷波动,利用蒙特卡洛模拟生成大量场景,并运用拉格朗日松弛法对复杂问题进行分解求解,从而实现全局最优或近似最优的充电调度计划。该方法有效降低了电网峰值负荷压力,提升了充电站运营效率与经济效益,同时兼顾用户充电便利性。 适合人群:具备一定电力系统、优化算法和Matlab编程基础的高校研究生、科研人员及从事智能电网、电动汽车相关领域的工程技术人员。 使用场景及目标:①应用于电动汽车充电站的日常运营管理,优化充电负荷分布;②服务于城市智能交通系统规划,提升电网与交通系统的协同水平;③作为学术研究案例,用于验证分散式优化算法在复杂能源系统中的有效性。 阅读建议:建议读者结合Matlab代码实现部分,深入理解蒙特卡洛模拟与拉格朗日松弛法的具体实施步骤,重点关注场景生成、约束处理与迭代收敛过程,以便在实际项目中灵活应用与改进。
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值