Taekwondo(dp)

本文介绍了一种解决跆拳道比赛中配对选手的问题算法,旨在通过数学模型找到最佳配对方案,使得所有配对选手的体重差异总和最小。采用排序与动态规划相结合的方法,确保比赛公平的同时实现算法效率。

Taekwondo is the name of a traditional Korean martial art and it is turned into a modern international
sport. It is adopted by IOC (International Olympic Committee) as an official game of 2000 Sydney
Olympic Games. In Taekwondo, there are individual competitions and team competitions. An individual
competition is conducted by two players and a team competition is a set of individual competitions.
For two groups of players, we are going to make a team competition where two players for each individual
competition are selected from each group. Note that players in each group can participate at most
one individual competition. For fair competition, weights of two players in each individual competition
must be very close. Given weights of players in two groups, you are to write a program to find pairs of
players so that the sum of the absolute differences of theweights of two players in each competition is
minimized.
Input
The input file consists of several test cases. The first line of the input file contains an integer representing
the number of test cases. The first line of each test case contains two integers. The first integer, n1,
is the number of players in the first group, and the second integer, n2, is the number of players in the
second group, where 1 ≤ n1, n2 ≤ 500. You have to make min{n1, n2} pairs of players. Each line of
the next n1 lines contains the weight of the player in the first group and the next n2 lines contain the
weights of players in the second group. Weights of players are in the range of 40.0 to 130.0. You may
assume that the precision of weight is one tenth.
Output
For each test case, your program reports the minimum of the sum of the absolute differences of the
weights of two players in each individual competition in the team competition.
Sample Input
2
2 3
44.9
50.0
77.2
86.4
59.8
4 2
44.9
50.0
77.2
86.4
59.8
58.9
Sample Output
42.1

23.8

题目大概:

就是给出了两组数 n个  m个 ,要求全部配对,使得所有  配对的数 的  abs(a【i】-b【j】)之和最小。

思路:

可以排序下,再dp。

dp【i】【j】代表,匹配了i对,到了第j个的时候的最小值。

那么,就需要把最短的那组数放到前面,这样才能符合这个dp定义,不会出错。

然后,由于我们已经排好序。所以这两组数之间所在区间范围,要么相离,要么相交。相交的话,那么匹配第i对数的时候,i前面的数b【j】是比 a【i】小的话,一顶定会找i 前面的数进行匹配,因为如果是找i,会出现交叉,一定不是最小的,可以画一下试试,如果匹配第i对数的时候,i前面的数b【j】是比 a【i】大的话,那么这一段是相离的,无论怎么匹配都是一样的结果,所以,我们可以让匹配到i对的时候,总是从第i个之后的b【j】和它匹配。

即匹配第一组第i个数时,要么是不匹配。要么是和第二组i之后的数匹配(包括i)。

代码:

#include <bits/stdc++.h>

using namespace std;
const int maxn=510;
double a[maxn],b[maxn];
double dp[maxn][maxn];
int main()
{
    int t;
    scanf("%d",&t);
    while(t--)
    {
        int n,m;
        memset(dp,0,sizeof(dp));
        memset(a,0,sizeof(a));
        memset(b,0,sizeof(b));
        scanf("%d%d",&n,&m);
        for(int i=1;i<=n;i++)
        {
            scanf("%lf",&a[i]);
        }
        for(int i=1;i<=m;i++)
        {
            scanf("%lf",&b[i]);
        }
        if(n>m)
        {
            for(int i=1;i<=n;i++)
            {
                double p=a[i];
                a[i]=b[i];
                b[i]=p;
            }
            int p=n;
            n=m;
            m=p;
        }
        sort(a+1,a+n+1);
        sort(b+1,b+m+1);
        dp[1][1]=fabs(a[1]-b[1]);
        for(int i=2;i<=m;i++)
        {
            dp[1][i]=min(dp[1][i-1],fabs(a[1]-b[i]));
        }
        for(int i=2;i<=n;i++)
        {
            dp[i][i]=dp[i-1][i-1]+fabs(a[i]-b[i]);
            for(int j=i+1;j<=m;j++)
            {
                dp[i][j]=min(dp[i][j-1],dp[i-1][j-1]+fabs(a[i]-b[j]));
            }
        }
        printf("%.1lf\n",dp[n][m]);
    }
    return 0;
}


内容概要:本文介绍了一个基于MATLAB实现的多目标粒子群优化算法(MOPSO)在无人机三维路径规划中的应用。该代码实现了完整的路径规划流程,包括模拟数据生成、障碍物随机生成、MOPSO优化求解、帕累托前沿分析、最优路径选择、代理模型训练以及丰富的可视化功能。系统支持用户通过GUI界面设置参数,如粒子数量、迭代次数、路径节点数等,并能一键运行完成路径规划与评估。代码采用模块化设计,包含详细的注释,同时提供了简洁版本,便于理解和二次开发。此外,系统还引入了代理模型(surrogate model)进行性能预测,并通过多种图表对结果进行全面评估。 适合人群:具备一定MATLAB编程基础的科研人员、自动化/控制/航空航天等相关专业的研究生或高年级本科生,以及从事无人机路径规划、智能优化算法研究的工程技术人员。 使用场景及目标:①用于教学演示多目标优化算法(如MOPSO)的基本原理与实现方法;②为无人机三维路径规划提供可复现的仿真平台;③支持对不同参数配置下的路径长度、飞行时间、能耗与安全风险之间的权衡进行分析;④可用于进一步扩展研究,如融合动态环境、多无人机协同等场景。 其他说明:该资源包含两份代码(详细注释版与简洁版),运行结果可通过图形界面直观展示,包括Pareto前沿、收敛曲线、风险热图、路径雷达图等,有助于深入理解优化过程与结果特性。建议使用者结合实际需求调整参数,并利用提供的模型导出功能将最优路径应用于真实系统。
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值