POJ 1469 二分图最大匹配

本文探讨了二分图最大匹配算法的应用场景,详细解释了如何通过最大匹配数判断课程与学生之间的分配是否合理,并提供了高效求解方法。

又是裸的二分图最大匹配。。注意最大匹配数等于课程数就ok了。。。。。。貌似做的都是水题。。。。。。。题目:

COURSES
Time Limit:1000MS Memory Limit:10000K
Total Submissions:11428 Accepted:4485

Description

Consider a group of N students and P courses. Each student visits zero, one or more than one courses. Your task is to determine whether it is possible to form a committee of exactly P students that satisfies simultaneously the conditions:

  • every student in the committee represents a different course (a student can represent a course if he/she visits that course)
  • each course has a representative in the committee

Input

Your program should read sets of data from the std input. The first line of the input contains the number of the data sets. Each data set is presented in the following format:

P N
Count1 Student1 1Student1 2... Student1 Count1
Count2 Student2 1Student2 2... Student2 Count2
...
CountP StudentP 1StudentP 2... StudentP CountP

The first line in each data set contains two positive integers separated by one blank: P (1 <= P <= 100) - the number of courses and N (1 <= N <= 300) - the number of students. The next P lines describe in sequence of the courses �from course 1 to course P, each line describing a course. The description of course i is a line that starts with an integer Count i (0 <= Count i <= N) representing the number of students visiting course i. Next, after a blank, you抣l find the Count i students, visiting the course, each two consecutive separated by one blank. Students are numbered with the positive integers from 1 to N.
There are no blank lines between consecutive sets of data. Input data are correct.

Output

The result of the program is on the standard output. For each input data set the program prints on a single line "YES" if it is possible to form a committee and "NO" otherwise. There should not be any leading blanks at the start of the line.

Sample Input

2
3 3
3 1 2 3
2 1 2
1 1
3 3
2 1 3
2 1 3
1 1

Sample Output

YES
NO
ac代码,,不过效率不高,,跑了600多ms,,,,欢迎大家提供高效代码

#include <iostream> #include <string.h> #include <vector> #include <cstdio> using namespace std; vector<int> aa[105]; int visted[305],flag[305]; bool dfs(int x){ for(int i=0;i<aa[x].size();++i){ if(!visted[aa[x][i]]){ visted[aa[x][i]]=1; if(!flag[aa[x][i]]||dfs(flag[aa[x][i]])){ flag[aa[x][i]]=x; return true; } } } return false; } int main(){ int kk; scanf("%d",&kk); while(kk--){ int p,n; memset(aa,0,sizeof(aa)); memset(flag,0,sizeof(flag)); scanf("%d%d",&p,&n); int num,x; for(int i=1;i<=p;++i){ scanf("%d",&num); while(num--){ scanf("%d",&x); aa[i].push_back(x); } } int sum=0; for(int i=1;i<=p;++i){ memset(visted,0,sizeof(visted)); if(dfs(i)) sum++; } if(sum==p) printf("YES\n"); else printf("NO\n"); } return 0; }

【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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