HDU-1158dp

Employment Planning

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/32768 K (Java/Others)
Total Submission(s): 6073    Accepted Submission(s): 2627


Problem Description
A project manager wants to determine the number of the workers needed in every month. He does know the minimal number of the workers needed in each month. When he hires or fires a worker, there will be some extra cost. Once a worker is hired, he will get the salary even if he is not working. The manager knows the costs of hiring a worker, firing a worker, and the salary of a worker. Then the manager will confront such a problem: how many workers he will hire or fire each month in order to keep the lowest total cost of the project.
 

Input
The input may contain several data sets. Each data set contains three lines. First line contains the months of the project planed to use which is no more than 12. The second line contains the cost of hiring a worker, the amount of the salary, the cost of firing a worker. The third line contains several numbers, which represent the minimal number of the workers needed each month. The input is terminated by line containing a single '0'.
 

Output
The output contains one line. The minimal total cost of the project.
 

Sample Input
3 4 5 6 10 9 11 0
 

Sample Output
199

题意:给n个项目,做一个项目一个月。雇佣一个人需要hire,工作一个月需要salary的钱,解雇一个人需要fire的钱。问做完n项目最少需要多少钱

思路:用dp[i][j]表示第i个项目j个人最少需要多少钱(我们可以从前面项目的人数推过来)

坑点:没说数据多大,尽量开到超时边缘

AC代码:

#include <iostream>
#include <cstring>
#include <algorithm>
using namespace std;
const int INF=0x7f7f7f7f;
int dp[20][10000],num[20];
int main()
{
    int n,i,j,k;
    int hire,fire,salary;
    int Max,Min;
    while (cin>>n&&n)
        {
            cin>>hire>>salary>>fire;
            Max=-INF;
            memset(dp,0,sizeof(dp));
            memset(num,0,sizeof(num));
            for (i=1;i<=n;i++)
                {
                    cin>>num[i];
                    Max=max(Max,num[i]);            //找到最多需要雇佣多少人
                }
            for (i=1;i<=Max;i++)
                dp[1][i]=i*hire+i*salary;        //第一个项目雇佣人的费用 ,初始化 
            for (i=2;i<=n;i++)
                for (j=num[i];j<=Max;j++)
                    {
                        Min=INF;                    //找到完成从i-1项目到i项目最少的钱 
                        for (k=num[i-1];k<=Max;k++)
                            if (k<j)
                                Min=min(Min,dp[i-1][k]+(j-k)*hire+j*salary);
                            else Min=min(Min,dp[i-1][k]+(k-j)*fire+j*salary);
                        dp[i][j]=Min;
                    }
            Min=INF;
            for (i=num[n];i<=Max;i++)
                Min=min(Min,dp[n][i]);
            cout<<Min<<endl;
        }
    return 0;
}


内容概要:本文围绕EKF SLAM(扩展卡尔曼滤波同步定位与地图构建)的性能展开多项对比实验研究,重点分析在稀疏与稠密landmark环境下、预测与更新步骤同时进行与非同时进行的情况下的系统性能差异,并进一步探讨EKF SLAM在有色噪声干扰下的鲁棒性表现。实验考虑了不确定性因素的影响,旨在评估不同条件下算法的定位精度与地图构建质量,为实际应用中EKF SLAM的优化提供依据。文档还提及多智能体系统在遭受DoS攻击下的弹性控制研究,但核心内容聚焦于SLAM算法的性能测试与分析。; 适合人群:具备一定机器人学、状态估计或自动驾驶基础知识的科研人员及工程技术人员,尤其是从事SLAM算法研究或应用开发的硕士、博士研究生和相关领域研发人员。; 使用场景及目标:①用于比较EKF SLAM在不同landmark密度下的性能表现;②分析预测与更新机制同步与否对滤波器稳定性与精度的影响;③评估系统在有色噪声等非理想观测条件下的适应能力,提升实际部署中的可靠性。; 阅读建议:建议结合MATLAB仿真代码进行实验复现,重点关注状态协方差传播、观测更新频率与噪声模型设置等关键环节,深入理解EKF SLAM在复杂环境下的行为特性。稀疏 landmark 与稠密 landmark 下 EKF SLAM 性能对比实验,预测更新同时进行与非同时进行对比 EKF SLAM 性能对比实验,EKF SLAM 在有色噪声下性能实验
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