LA3266 - Tian Ji -- The Horse Racing

本文介绍了一个著名的中国历史故事——田忌赛马,并通过现代算法视角重新审视了这个问题。提出了如何利用匹配算法来确定最优的比赛策略,确保田忌能够在与国王的赛马比赛中获得尽可能多的胜利。

Here is a famous story in Chinese history.

That was about 2300 years ago. General Tian Ji was a high official in the country Qi. He likes to play horse racing with the king and others.

Both of Tian and the king have three horses in different classes, namely, regular, plus, and super. The rule is to have three rounds in a match; each of the horses must be used in one round. The winner of a single round takes two hundred silver dollars from the loser.

Being the most powerful man in the country, the king has so nice horses that in each class his horse is better than Tian's. As a result, each time the king takes six hundred silver dollars from Tian.

Tian Ji was not happy about that, until he met Sun Bin, one of the most famous generals in Chinese history. Using a little trick due to Sun, Tian Ji brought home two hundred silver dollars and such a grace in the next match.

It was a rather simple trick. Using his regular class horse race against the super class from the king, they will certainly lose that round. But then his plus beat the king's regular, and his super beat the king's plus. What a simple trick. And how do you think of Tian Ji, the high ranked official in China?

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Were Tian Ji lives in nowadays, he will certainly laugh at himself. Even more, were he sitting in the ACM contest right now, he may discover that the horse racing problem can be simply viewed as finding the maximum matching in a bipartite graph. Draw Tian's horses on one side, and the king's horses on the other. Whenever one of Tian's horses can beat one from the king, we draw an edge between them, meaning we wish to establish this pair. Then, the problem of winning as many rounds as possible is just to find the maximum matching in this graph. If there are ties, the problem becomes more complicated, he needs to assign weights 0, 1, or -1 to all the possible edges, and find a maximum weighted perfect matching...

However, the horse racing problem is a very special case of bipartite matching. The graph is decided by the speed of the horses -- a vertex of higher speed always beat a vertex of lower speed. In this case, the weighted bipartite matching algorithm is a too advanced tool to deal with the problem.

In this problem, you are asked to write a program to solve this special case of matching problem.

Input 

The input consists of up to 50 test cases. Each case starts with a positive integer n ( n$ \le$1000) on the first line, which is the number of horses on each side. The next n integers on the second line are the speeds of Tian's horses. Then the next n integers on the third line are the speeds of the king's horses. The input ends with a line that has a single `0' after the last test case.

Output 

For each input case, output a line containing a single number, which is the maximum money Tian Ji will get, in silver dollars.

Sample Input 

3 
92 83 71  
95 87 74 
2 
20 20 
20 20 
2 
20 19 
22 18 
0

Sample Output 

200 
0 

0

/*
1.田忌最快的马如果比齐王快,就派遣这一匹
2.田忌最快的马如果比齐王慢,用最慢的马去跟齐王比
3.相等情况,要看最慢马的大小
        如果田忌最慢的马要快,就先比慢的马,否则用慢的对上齐王快的
*/
#include <iostream>
#include <stdio.h>
#include <cstring>
#include <algorithm>
#include <queue>
#include <map>
#include <cmath>
using namespace std;
typedef long long ll;
const int MAXN = 1010;
int tian[MAXN], qi[MAXN];
int main()
{
    int n;
    while(scanf("%d",&n)!=EOF)
    {
        if(n==0)break;
         int i, j;
         for(i=0; i<n; i++)
         {
             scanf("%d",&tian[i]);
         }
         for(i=0; i<n; i++)
         {
             scanf("%d",&qi[i]);
         }
         sort(tian,tian+n);
         sort(qi,qi+n);
         int ans = 0;
         int tian0 = 0, tian1 = n-1, qi0 = 0, qi1 = n-1;
         while(n--)
         {
             //最快的马,田忌的快
             if(qi[qi1] < tian[tian1])
             {
                 qi1--;
                 tian1--;
                 ans++;
             }else if(qi[qi1] > tian[tian1])//最快的马,田忌的慢
             {
                 qi1--;
                 tian0++;
                 ans--;
             }else{
                 if(qi[qi0] < tian[tian0])//最慢的比齐王快,先比最慢的
                {
                    tian0++;
                    qi0++;
                    ans++;
                }else{//最慢的相等就,用最慢的对齐王快的
                    if(tian[tian0] < qi[qi1])ans--;
                    qi1--;
                    tian0++;
                }
             }
         }
         printf("%d\n",ans*200 );
    }
    return 0;
}


内容概要:本文围绕六自由度机械臂的人工神经网络(ANN)设计展开,重点研究了正向与逆向运动学求解、正向动力学控制以及基于拉格朗日-欧拉法推导逆向动力学方程,并通过Matlab代码实现相关算法。文章结合理论推导与仿真实践,利用人工神经网络对复杂的非线性关系进行建模与逼近,提升机械臂运动控制的精度与效率。同时涵盖了路径规划中的RRT算法与B样条优化方法,形成从运动学到动力学再到轨迹优化的完整技术链条。; 适合人群:具备一定机器人学、自动控制理论基础,熟悉Matlab编程,从事智能控制、机器人控制、运动学六自由度机械臂ANN人工神经网络设计:正向逆向运动学求解、正向动力学控制、拉格朗日-欧拉法推导逆向动力学方程(Matlab代码实现)建模等相关方向的研究生、科研人员及工程技术人员。; 使用场景及目标:①掌握机械臂正/逆运动学的数学建模与ANN求解方法;②理解拉格朗日-欧拉法在动力学建模中的应用;③实现基于神经网络的动力学补偿与高精度轨迹跟踪控制;④结合RRT与B样条完成平滑路径规划与优化。; 阅读建议:建议读者结合Matlab代码动手实践,先从运动学建模入手,逐步深入动力学分析与神经网络训练,注重理论推导与仿真实验的结合,以充分理解机械臂控制系统的设计流程与优化策略。
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