hdu1260 Tickets(DP)

本文介绍了一个关于电影票销售的算法问题,通过动态规划的方法求解售票员将票卖给特定数量顾客所需的最短时间。问题允许两种票务处理方式:单独售卖给一位顾客或同时售卖给两位相邻顾客。

题目链接:

http://acm.hdu.edu.cn/showproblem.php?pid=1260


Tickets

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/32768 K (Java/Others)
Total Submission(s): 2897    Accepted Submission(s): 1413


Problem Description
Jesus, what a great movie! Thousands of people are rushing to the cinema. However, this is really a tuff time for Joe who sells the film tickets. He is wandering when could he go back home as early as possible.
A good approach, reducing the total time of tickets selling, is let adjacent people buy tickets together. As the restriction of the Ticket Seller Machine, Joe can sell a single ticket or two adjacent tickets at a time.
Since you are the great JESUS, you know exactly how much time needed for every person to buy a single ticket or two tickets for him/her. Could you so kind to tell poor Joe at what time could he go back home as early as possible? If so, I guess Joe would full of appreciation for your help.
 

Input
There are N(1<=N<=10) different scenarios, each scenario consists of 3 lines:
1) An integer K(1<=K<=2000) representing the total number of people;
2) K integer numbers(0s<=Si<=25s) representing the time consumed to buy a ticket for each person;
3) (K-1) integer numbers(0s<=Di<=50s) representing the time needed for two adjacent people to buy two tickets together.
 

Output
For every scenario, please tell Joe at what time could he go back home as early as possible. Every day Joe started his work at 08:00:00 am. The format of time is HH:MM:SS am|pm.
 

Sample Input
  
2 2 20 25 40 1 8
 

Sample Output
  
08:00:40 am 08:00:08 am
 

Source

题意:求售票员把票卖给K个人所需的最短时间,票有2种方式售卖,一种是单独卖给一个顾客,另一种就是卖给相邻的两个顾客(_(:з」∠)_看到这里就觉得是dp了)

解题思路:首先,票有2种方式售卖,就猜想有2个转移方程。这时我们先设dp[i]为卖给前i个人(包含第i个人)的最短时间,很明显有2种状态可以转移到这个状态上。

①第i个人单独购买票。即dp[i-1] + s[i] ; (s[i]为第i个人单独购票所需的时间)

②第i和i-1一起买票。  即dp[i-2] +d[i-1] ;(d[i]第i和第i-1一起买票所需的时间)

所以我们就可以很简单得出方程 dp[i] = min { dp[i-1] + s[i] , dp[i -2] +d[i-1]}


AC代码如下:

#include<iostream>
#include<algorithm>
#include<cstdio>
#include<cstring>
#include<set>
#include<cmath>
#include<vector>
#include<map>
using namespace std;
typedef long long ll;
const int maxn = 2050;
const int INF = 1e+9;
int d[maxn];
int s[maxn];
int dp[maxn];
char str[][10]={"pm","am"};
int main()
{
    int n;
    cin >> n ;
    while(n--)
    {
        int k1 ;
        scanf("%d",&k1);
        for(int i = 1 ; i <= k1 ; i ++)
            scanf("%d",&s[i]);
        int k2 = k1-1;
        for(int i = 1 ; i <= k2 ; i ++)
            scanf("%d",&d[i]);
        dp[0] = 0;
        for(int i = 1; i <= k1; i ++)
            dp[i] = INF;
        for(int i = 1 ; i <= k1; i ++)
        {
            dp[i] = min(dp[i],dp[i-1]+s[i]);
            if(i>=2)
                dp[i] =min(dp[i],dp[i-2]+d[i-1]);
        }
        int cnt1 = dp[k1]/3600;
        int cnt2 = (dp[k1]-cnt1*3600)/60;
        int cnt3 = dp[k1]%60;
        int HH,MM,SS,flag=1;
        HH = 8+cnt1 ;
        MM = cnt2;
        SS = cnt3;
        if(HH>=12)
        {
            HH-=12;
            flag = 0;
        }
        printf("%02d:%02d:%02d %s\n",HH,MM,SS,str[flag]);
    }

    return 0 ;
}




基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制方法。通过结合数据驱动技术与Koopman算子理论,将非线性系统动态近似为高维线性系统,进而利用递归神经网络(RNN)建模并实现系统行为的精确预测。文中详细阐述了模型构建流程、线性化策略及在预测控制中的集成应用,并提供了完整的Matlab代码实现,便于科研人员复现实验、优化算法并拓展至其他精密控制系统。该方法有效提升了纳米级定位系统的控制精度与动态响应性能。; 适合人群:具备自动控制、机器学习或信号处理背景,熟悉Matlab编程,从事精密仪器控制、智能制造或先进控制算法研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①实现非线性动态系统的数据驱动线性化建模;②提升纳米定位平台的轨迹跟踪与预测控制性能;③为高精度控制系统提供可复现的Koopman-RNN融合解决方案; 阅读建议:建议结合Matlab代码逐段理解算法实现细节,重点关注Koopman观测矩阵构造、RNN训练流程与模型预测控制器(MPC)的集成方式,鼓励在实际硬件平台上验证并调整参数以适应具体应用场景。
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