(DS 《算法竞赛入门经典》)LA 3644 X-Plosives(并查集)

探讨了使用并查集解决一种特殊的爆炸物组合问题,该问题涉及如何判断不同的化合物组合是否会形成爆炸物,以及在一系列输入中如何计算被拒绝的化合物组合数量。

解题思路:

      并查集



A secret service developed a new kind of explosive that attain its volatile property only when a specific
association of products occurs. Each product is a mix of two different simple compounds, to which we
call a binding pair. If N > 2, then mixing N different binding pairs containing N simple compounds
creates a powerful explosive. For example, the binding pairs A+B, B+C, A+C (three pairs, three
compounds) result in an explosive, while A+B, B+C, A+D (three pairs, four compounds) does not.
You are not a secret agent but only a guy in a delivery agency with one dangerous problem: receive
binding pairs in sequential order and place them in a cargo ship. However, you must avoid placing in
the same room an explosive association. So, after placing a set of pairs, if you receive one pair that
might produce an explosion with some of the pairs already in stock, you must refuse it, otherwise, you
must accept it.
An example. Lets assume you receive the following sequence: A+B, G+B, D+F, A+E, E+G,
F+H. You would accept the first four pairs but then refuse E+G since it would be possible to make the
following explosive with the previous pairs: A+B, G+B, A+E, E+G (4 pairs with 4 simple compounds).
Finally, you would accept the last pair, F+H.
Compute the number of refusals given a sequence of binding pairs.
Input
The input will contain several test cases, each of them as described below. Consecutive
test cases are separated by a single blank line.
Instead of letters we will use integers to represent compounds. The input contains several lines.
Each line (except the last) consists of two integers (each integer lies between 0 and 105
) separated by
a single space, representing a binding pair.
Each test case ends in a line with the number ‘-1’. You may assume that no repeated binding pairs
appears in the input.
Output
For each test case, the output must follow the description below.
A single line with the number of refusals.
Sample Input
1 2
3 4
3 5
3 1
2 3
4 1
2 6
6 5
-1
Sample Output
3


代码如下:

/*
 * LA3644.cpp
 *
 *  Created on: 2014年12月30日
 *      Author: Administrator
 */

#include <iostream>
#include <cstdio>
#include <cstring>


using namespace std;

const int maxn = 100000;

int father[maxn + 10];



int find(int x){
	if(x == father[x]){
		return x;
	}

	return father[x] = find(father[x]);
}




int main(){
	int x,y;
	while(scanf("%d",&x) == 1){
		int result = 0;

		int i;
		for(i = 0 ; i <= maxn ; ++i){
			father[i] = i;
		}

		while(x != -1){
			scanf("%d",&y);

			int fx = find(x);
			int fy = find(y);

			if(fx != fy){
//				join(fx,fy);
				father[fy] = fx;
			}else{
				result++;
			}

			scanf("%d",&x);
		}

		printf("%d\n",result);
	}

	return 0;
}






内容概要:本文介绍了一个基于冠豪猪优化算法(CPO)的无人机三维路径规划项目,利用Python实现了在复杂三维环境中为无人机规划安全、高效、低能耗飞行路径的完整解决方案。项目涵盖空间环境建模、无人机动力学约束、路径编码、多目标代价函数设计以及CPO算法的核心实现。通过体素网格建模、动态障碍物处理、路径平滑技术和多约束融合机制,系统能够在高维、密集障碍环境下快速搜索出满足飞行可行性、安全性与能效最优的路径,并支持在线重规划以适应动态环境变化。文中还提供了关键模块的代码示例,包括环境建模、路径评估和CPO优化流程。; 适合人群:具备一定Python编程基础和优化算法基础知识,从事无人机、智能机器人、路径规划或智能优化算法研究的相关科研人员与工程技术人员,尤其适合研究生及有一定工作经验的研发工程师。; 使用场景及目标:①应用于复杂三维环境下的无人机自主导航与避障;②研究智能优化算法(如CPO)在路径规划中的实际部署与性能优化;③实现多目标(路径最短、能耗最低、安全性最高)耦合条件下的工程化路径求解;④构建可扩展的智能无人系统决策框架。; 阅读建议:建议结合文中模型架构与代码示例进行实践运行,重点关注目标函数设计、CPO算法改进策略与约束处理机制,宜在仿真环境中测试不同场景以深入理解算法行为与系统鲁棒性。
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