Educational Codeforces Round 5

在一个由街道和大道组成的网格中,每条街与大道交汇处有一家餐厅。主人公杰克和艾玛将通过一种特殊的方式选择一家餐厅共进晚餐:艾玛先选街道,杰克再从该街道上选择他认为最经济的餐厅。此过程体现了两人的最优选择策略。


B. Dinner with Emma
time limit per test
1 second
memory limit per test
256 megabytes
input
standard input
output
standard output

Jack decides to invite Emma out for a dinner. Jack is a modest student, he doesn't want to go to an expensive restaurant. Emma is a girl with high taste, she prefers elite places.

Munhattan consists of n streets and m avenues. There is exactly one restaurant on the intersection of each street and avenue. The streets are numbered with integers from 1 to n and the avenues are numbered with integers from 1 to m. The cost of dinner in the restaurant at the intersection of the i-th street and the j-th avenue is cij.

Jack and Emma decide to choose the restaurant in the following way. Firstly Emma chooses the street to dinner and then Jack chooses the avenue. Emma and Jack makes their choice optimally: Emma wants to maximize the cost of the dinner, Jack wants to minimize it. Emma takes into account that Jack wants to minimize the cost of the dinner. Find the cost of the dinner for the couple in love.

Input

The first line contains two integers n, m (1 ≤ n, m ≤ 100) — the number of streets and avenues in Munhattan.

Each of the next n lines contains m integers cij (1 ≤ cij ≤ 109) — the cost of the dinner in the restaurant on the intersection of the i-th street and the j-th avenue.

Output

Print the only integer a — the cost of the dinner for Jack and Emma.

Sample test(s)
Input
3 4
4 1 3 5
2 2 2 2
5 4 5 1
Output
2
Input
3 3
1 2 3
2 3 1
3 1 2
Output
1
Note

In the first example if Emma chooses the first or the third streets Jack can choose an avenue with the cost of the dinner 1. So she chooses the second street and Jack chooses any avenue. The cost of the dinner is 2.

In the second example regardless of Emma's choice Jack can choose a restaurant with the cost of the dinner 1.



#include <bits/stdc++.h>

using namespace std;

#define chfor(ch,cha,chn) for(char ch=cha;ch<chn;ch++)
#define rep(i,a,n) for (int i=a;i<n;i++)
#define per(i,a,n) for (int i=n-1;i>=a;i--)
#define fi first
#define se second 
#define M 1000000  
typedef long long ll;
const int shi=123;             
 

int main(void){  
     int n,m;
     cin >> n >> m;
   int a[n][m];
   for(int i =0;i<n;i++)
     for(int j=0;j<m;j++)
           cin >> a[i][j]; 
   int b[n],k=0;
   
   for(int i=0;i<n;i++)
{
    b[i]=a[i][0];
    for (int j=0;j<m;j++)
       if(b[i]>a[i][j])
            b[i]=a[i][j];
}
k=b[0];
   for(int i=0;i<n;i++)
          if(k<b[i]) 
               k=b[i];
 cout << k << endl; 

      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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