ural 1018 Binary Apple Tree(树形dp)

Description

Let’s imagine how apple tree looks in binary computer world. You’re right, it looks just like a binary tree, i.e. any biparous branch splits up to exactly two new branches. We will enumerate by integers the root of binary apple tree, points of branching and the ends of twigs. This way we may distinguish different branches by their ending points. We will assume that root of tree always is numbered by 1 and all numbers used for enumerating are numbered in range from 1 to N, where N is the total number of all enumerated points. For instance in the picture below N is equal to 5. Here is an example of an enumerated tree with four branches:
2 5
\ /
3 4
\ /
1
As you may know it’s not convenient to pick an apples from a tree when there are too much of branches. That’s why some of them should be removed from a tree. But you are interested in removing branches in the way of minimal loss of apples. So your are given amounts of apples on a branches and amount of branches that should be preserved. Your task is to determine how many apples can remain on a tree after removing of excessive branches.

Input

First line of input contains two numbers: N and Q ( 2 ≤ N ≤ 100; 1 ≤ Q ≤ N − 1 ). N denotes the number of enumerated points in a tree. Q denotes amount of branches that should be preserved. Next N − 1 lines contains descriptions of branches. Each description consists of a three integer numbers divided by spaces. The first two of them define branch by it’s ending points. The third number defines the number of apples on this branch. You may assume that no branch contains more than 30000 apples.

Output

Output should contain the only number — amount of apples that can be preserved. And don’t forget to preserve tree’s root 😉

Input

5 2
1 3 1
1 4 10
2 3 20
3 5 20

Output

21

题意

给一棵边有权值的二叉树,节点编号为1~n,1是根节点。求砍掉一些边,只保留q条边,这q条边构成的子树的根节点要求是1,问这颗子树的最大权值是多少?

题解

dp(i, j) 表示子树i,保留j个节点(注意是节点)的最大权值。每条边的权值,把它看作是连接的两个节点中的儿子节点的权值。那么,就可以对所有i的子树做分组背包,即每个子树可以选择1,2,…j-1条边分配给它。

状态转方程:

dp(i, j) = max{max{dp(i, j-k) +dp(v, k) | 1<=k<j} | v是i的儿子}
ans = f(1, q+1)

AC代码

#include<bits/stdc++.h>
using namespace std;
const int maxn=110;
const int inf=0x3f3f3f3f;
int n,q;
vector<pair<int,int>>adj[maxn];
int tot[maxn];
int dp[maxn][maxn];
int dfs(int u,int fa){
	tot[u]=1;
	for(int i=0;i<adj[u].size();i++){
		int v=adj[u][i].first;
		if(v==fa) continue;
		tot[u]+=dfs(v,u);
	}
	for(int i=0;i<adj[u].size();i++){
		int v=adj[u][i].first;
		int w=adj[u][i].second;
		if(v==fa) continue;
		for (int i=tot[u];i>0;i--){
            for(int j=1;j<i&&j<=tot[v];j++){
                dp[u][i]=max(dp[u][i],dp[u][i-j]+dp[v][j]+w); 
            } 
        }
    }
    	return tot[u];
}
int main(){
	while(~scanf("%d%d",&n,&q)){
		for(int i=0;i<=n;i++) adj[i].clear();
		for(int i=0;i<n-1;i++){
			int u,v,w;
			scanf("%d%d%d",&u,&v,&w);
			adj[u].push_back(make_pair(v,w));
			adj[v].push_back(make_pair(u,w));
			memset(dp,0,sizeof(dp));
			dfs(1,-1);
		}	
		printf("%d\n",dp[1][q+1]);
		return 0;
	}
}

参考博客:https://blog.youkuaiyun.com/shuangde800/article/details/10834207

分数阶傅里叶变换(Fractional Fourier Transform, FRFT)是对传统傅里叶变换的拓展,它通过非整数阶的变换方式,能够更有效地处理非线性信号以及涉及时频局部化的问题。在信号处理领域,FRFT尤其适用于分析非平稳信号,例如在雷达、声纳和通信系统中,对线性调频(Linear Frequency Modulation, LFM)信号的分析具有显著优势。LFM信号是一种频率随时间线性变化的信号,因其具有宽频带和良好的时频分辨率,被广泛应用于雷达和通信系统。FRFT能够更精准地捕捉LFM信号的时间和频率信息,相比普通傅里叶变换,其性能更为出色。 MATLAB是一种强大的数值计算和科学计算工具,拥有丰富的函数库和用户友好的界面。在MATLAB中实现FRFT,通常需要编写自定义函数或利用信号处理工具箱中的相关函数。例如,一个名为“frft”的文件可能是用于执行分数阶傅里叶变换的MATLAB脚本或函数,并展示其在信号处理中的应用。FRFT的正确性验证通常通过对比变换前后信号的特性来完成,比如评估信号的重构质量、信噪比等。具体而言,可以通过计算原始信号与经过FRFT处理后的信号之间的相似度,或者对比LFM信号的关键参数(如初始频率、扫频率和持续时间)是否在变换后得到准确恢复。 在MATLAB代码实现中,通常包含以下步骤:首先,生成LFM信号模型,设定其初始频率、扫频率、持续时间和采样率等参数;其次,利用自定义的frft函数对LFM信号进行分数阶傅里叶变换;接着,使用MATLAB的可视化工具(如plot或imagesc)展示原始信号的时域和频域表示,以及FRFT后的结果,以便直观对比;最后,通过计算均方误差、峰值信噪比等指标来评估FRFT的性能。深入理解FRFT的数学原理并结合MATLAB编程技巧,可以实现对LFM信号的有效分析和处理。这个代码示例不仅展示了理论知识在
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