hdu3001 三进制状态压缩+dp

本文介绍了一个旅行路径规划问题,目标是最小化访问多个城市总费用的同时确保每个城市最多被访问两次。采用三进制状态表示法来跟踪每个城市的访问次数,并通过动态规划求解最优路径。

Travelling

Time Limit: 6000/3000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others)
Total Submission(s): 3477    Accepted Submission(s): 1084


Problem Description
After coding so many days,Mr Acmer wants to have a good rest.So travelling is the best choice!He has decided to visit n cities(he insists on seeing all the cities!And he does not mind which city being his start station because superman can bring him to any city at first but only once.), and of course there are m roads here,following a fee as usual.But Mr Acmer gets bored so easily that he doesn't want to visit a city more than twice!And he is so mean that he wants to minimize the total fee!He is lazy you see.So he turns to you for help.
 

Input
There are several test cases,the first line is two intergers n(1<=n<=10) and m,which means he needs to visit n cities and there are m roads he can choose,then m lines follow,each line will include three intergers a,b and c(1<=a,b<=n),means there is a road between a and b and the cost is of course c.Input to the End Of File.
 

Output
Output the minimum fee that he should pay,or -1 if he can't find such a route.
 

Sample Input
2 1 1 2 100 3 2 1 2 40 2 3 50 3 3 1 2 3 1 3 4 2 3 10
 

Sample Output
100 90 7
 

单排(dp)果然博大精深。三进制状态的值表示当前点已取第几次。哦,先说下题意吧。。。题意应该就是有个妹纸去旅游,然后有N个地方,她要游遍所有的地方但是每个地方最多只能去两次!问最短距离是多少。两次,第一次写被这两次坑了,没注意到,后来三进制可以表示到一个地方去了几次,当所有地方都遍历到,就可以进行取值,当然要是最小的。条件也有几个。初始点要设为0.

#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <algorithm>
#include <iostream>
#include <cmath>
#include <queue>
#include <map>
#include <stack>
#include <list>
#include <vector>
using namespace std;
#define LL __int64
int dis[15][15],kfc[60000][15],dp[60000][15];
int t,n,j,m,i,u,v,d;
int sta[12]={0,1,3,9,27,81,243,729,2187,6561,19683,59049};
#define INF 0x7fffff1f
#define min(a,b) a>b?b:a
int main()
{
	for (i=0;i<59049;i++)
	{
		t=i;
		for (j=1;j<=10;j++)
		{
			kfc[i][j]=t % 3;
			t/=3;
			if (t==0) break;
		}
	}
	while (2==scanf("%d%d",&n,&m))
	{
		for (i=1;i<60000;i++)
			for (j=1;j<=12;j++)
			{
				if (i<15)
					dis[i][j]=INF;
				dp[i][j]=INF;
			}
		for (i=1;i<=m;i++)
		{
			scanf("%d%d%d",&u,&v,&d);
			dis[u][v]=dis[v][u]=min(dis[u][v],d);
		}
		for (i=1;i<=n;i++)
			dp[sta[i]][i]=0;
		int ans=INF;	
		for (t=0;t<sta[n+1];t++)
		{
			int find=1;
			for (i=1;i<=n;i++)
			{
				if (kfc[t][i]==0) find=0;
				if (dp[t][i]==INF) continue;
				for (j=1;j<=n;j++)
				{
					if (j==i || kfc[t][j]==2 || dis[i][j]==INF) continue;
					int newt=t+sta[j];
					dp[newt][j]=min(dp[newt][j],dp[t][i]+dis[i][j]);
				}
			}
			if (find)
				for (i=1;i<=n;i++)
					ans=min(ans,dp[t][i]);
		}
		if (ans==INF) ans=-1;
		cout<<ans<<endl;
	}
	return 0;
}



内容概要:本文提出了一种基于融合鱼鹰算法和柯西变异的改进麻雀优化算法(OCSSA),用于优化变分模态分解(VMD)的参数,进而结合卷积神经网络(CNN)与双向长短期记忆网络(BiLSTM)构建OCSSA-VMD-CNN-BILSTM模型,实现对轴承故障的高【轴承故障诊断】基于融合鱼鹰和柯西变异的麻雀优化算法OCSSA-VMD-CNN-BILSTM轴承诊断研究【西储大学数据】(Matlab代码实现)精度诊断。研究采用西储大学公开的轴承故障数据集进行实验验证,通过优化VMD的模态数和惩罚因子,有效提升了信号分解的准确性与稳定性,随后利用CNN提取故障特征,BiLSTM捕捉时间序列的深层依赖关系,最终实现故障类型的智能识别。该方法在提升故障诊断精度与鲁棒性方面表现出优越性能。; 适合人群:具备一定信号处理、机器学习基础,从事机械故障诊断、智能运维、工业大数据分析等相关领域的研究生、科研人员及工程技术人员。; 使用场景及目标:①解决传统VMD参数依赖人工经验选取的问题,实现参数自适应优化;②提升复杂工况下滚动轴承早期故障的识别准确率;③为智能制造与预测性维护提供可靠的技术支持。; 阅读建议:建议读者结合Matlab代码实现过程,深入理解OCSSA优化机制、VMD信号分解流程以及CNN-BiLSTM网络架构的计逻辑,重点关注参数优化与故障分类的联动关系,并可通过更换数据集进一步验证模型泛化能力。
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