HDU-1213 How Many Tables

本文介绍了一种使用并查集算法解决朋友分组问题的方法。通过构建并查集数据结构,实现快速查找与合并操作,有效解决了如何最少分配餐桌数量的问题。文章详细解释了并查集的工作原理及其实现细节。
Problem Description
Today is Ignatius' birthday. He invites a lot of friends. Now it's dinner time. Ignatius wants to know how many tables he needs at least. You have to notice that not all the friends know each other, and all the friends do not want to stay with strangers.

One important rule for this problem is that if I tell you A knows B, and B knows C, that means A, B, C know each other, so they can stay in one table.

For example: If I tell you A knows B, B knows C, and D knows E, so A, B, C can stay in one table, and D, E have to stay in the other one. So Ignatius needs 2 tables at least.
 

Input
The input starts with an integer T(1<=T<=25) which indicate the number of test cases. Then T test cases follow. Each test case starts with two integers N and M(1<=N,M<=1000). N indicates the number of friends, the friends are marked from 1 to N. Then M lines follow. Each line consists of two integers A and B(A!=B), that means friend A and friend B know each other. There will be a blank line between two cases.
 

Output

For each test case, just output how many tables Ignatius needs at least. Do NOT print any blanks.


并查集。如果是朋友,放到同一个集合里。求数有多少个集合,数有多少个代表元就行了。

#include<iostream>
#include<vector>
using namespace std;

struct node {
	int parent;
	int rank;
};

int Find(vector<node>& v, int x) {
	vector<int> path;
	while (v[x].parent != x) {
		path.push_back(x);
		x = v[x].parent;
	}
	for (int i = 0; i != path.size(); i++)
		v[path[i]].parent = x;
	return x;
}

void Union(vector<node>& v, int x, int y) {
	x = Find(v, x);
	y = Find(v, y);
	if (v[x].rank < v[y].rank) {
		v[x].parent = y;
	}
	else {
		v[y].parent = x;
		if (v[x].rank == v[y].rank)
			v[x].rank++;
	}
}

int main() {
	int case_number; cin >> case_number;
	for (int i = 0; i < case_number; i++) {
		int n, m; cin >> n >> m;
		vector<node> v(n);
		for (int i = 0; i < n; i++) {
			v[i].parent = i;
			v[i].rank = 1;
		}
		for (int j = 0; j < m; j++) {
			int n1, n2; cin >> n1 >> n2;
			Union(v, n1 - 1, n2 - 1);
		}
		vector<bool> isParent(n);
		for (int i = 0; i < n; i++) {
			isParent[Find(v, i)] = true;
		}
		int result = 0;
		for (int i = 0; i < n; i++)
			if (isParent[i])
				result++;
		cout << result << endl;
	}
}


### 关于HDU - 6609 的题目解析 由于当前未提供具体关于 HDU - 6609 题目的详细描述,以下是基于一般算法竞赛题型可能涉及的内容进行推测和解答。 #### 可能的题目背景 假设该题目属于动态规划类问题(类似于多重背包问题),其核心在于优化资源分配或路径选择。此类问题通常会给出一组物品及其属性(如重量、价值等)以及约束条件(如容量限制)。目标是最优地选取某些物品使得满足特定的目标函数[^2]。 #### 动态转移方程设计 如果此题确实是一个变种的背包问题,则可以采用如下状态定义方法: 设 `dp[i][j]` 表示前 i 种物品,在某种条件下达到 j 值时的最大收益或者最小代价。对于每一种新加入考虑范围内的物体 k ,更新规则可能是这样的形式: ```python for i in range(n): for s in range(V, w[k]-1, -1): dp[s] = max(dp[s], dp[s-w[k]] + v[k]) ``` 这里需要注意边界情况处理以及初始化设置合理值来保证计算准确性。 另外还有一种可能性就是它涉及到组合数学方面知识或者是图论最短路等相关知识点。如果是后者的话那么就需要构建相应的邻接表表示图形结构并通过Dijkstra/Bellman-Ford/Floyd-Warshall等经典算法求解两点间距离等问题了[^4]。 最后按照输出格式要求打印结果字符串"Case #X: Y"[^3]。 #### 示例代码片段 下面展示了一个简单的伪代码框架用于解决上述提到类型的DP问题: ```python def solve(): t=int(input()) res=[] cas=1 while(t>0): n,k=list(map(int,input().split())) # Initialize your data structures here ans=find_min_unhappiness() # Implement function find_min_unhappiness() res.append(f'Case #{cas}: {round(ans)}') cas+=1 t-=1 print("\n".join(res)) solve() ```
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