HDU 2955-Robberies(01背包+概率)

解决一个经典的概率与收益优化问题,即如何在多个目标中选择最佳组合以最大化收益同时确保被抓概率低于可接受范围。

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Robberies

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others)
Total Submission(s): 28110    Accepted Submission(s): 10323


Problem Description
The aspiring Roy the Robber has seen a lot of American movies, and knows that the bad guys usually gets caught in the end, often because they become too greedy. He has decided to work in the lucrative business of bank robbery only for a short while, before retiring to a comfortable job at a university.


For a few months now, Roy has been assessing the security of various banks and the amount of cash they hold. He wants to make a calculated risk, and grab as much money as possible.


His mother, Ola, has decided upon a tolerable probability of getting caught. She feels that he is safe enough if the banks he robs together give a probability less than this.
 

Input
The first line of input gives T, the number of cases. For each scenario, the first line of input gives a floating point number P, the probability Roy needs to be below, and an integer N, the number of banks he has plans for. Then follow N lines, where line j gives an integer Mj and a floating point number Pj .
Bank j contains Mj millions, and the probability of getting caught from robbing it is Pj .
 

Output
For each test case, output a line with the maximum number of millions he can expect to get while the probability of getting caught is less than the limit set.

Notes and Constraints
0 < T <= 100
0.0 <= P <= 1.0
0 < N <= 100
0 < Mj <= 100
0.0 <= Pj <= 1.0
A bank goes bankrupt if it is robbed, and you may assume that all probabilities are independent as the police have very low funds.
 

Sample Input

 
3 0.04 3 1 0.02 2 0.03 3 0.05 0.06 3 2 0.03 2 0.03 3 0.05 0.10 3 1 0.03 2 0.02 3 0.05
 

Sample Output

 
2 4 6
 

Source
题目大意:一个人去抢劫银行,有n个银行给他抢,抢第i个银行的利润为Mi被抓的概率为Pi,让你求在被抓的概率不超过限定概率的情况下,能抢到的钱最多是多少?


解题思路:这里牵扯到了概率问题,不知到大家还记得高中的知识吗?题目要求,不要被抓到,所以我们在抢每个银行的时候都不能被抓,所以 不被抓的概率为所抢银行不被抓的概率的乘积为 p =(1-p1)*(1-p2)....,最后输出满足1-p<=maxp的最大利润就可以了。我们dp[j] = max(dp[j],  dp[j-M[i]]]*(1-Pi))表示抢了j块钱不被抓的最大概率,最后我们贪心一下即可。

AC代码:

#include<stdio.h>
#include<string.h>
#include<iostream>
#include<string>
#include<algorithm>
#include<set>
#include<queue>
#define N 110

using namespace std;

int T, n, m[N], maxm;
double p[N], dp[N*100], maxp;

int main()
{
    scanf("%d", &T);
    while(T --) {
        memset(dp, 0, sizeof(dp));
        scanf("%lf%d", &maxp, &n);
        maxm = 0;
        dp[0] = 1;
        for(int i = 0; i < n; i ++) {
            scanf("%d%lf", &m[i], &p[i]);
            maxm += m[i];
        }
        for(int i = 0; i < n; i ++) {
            for(int j = maxm; j >= m[i]; j --) {
                dp[j] = max(dp[j], dp[j-m[i]]*(1-p[i]));
            }
        }
        for(int i = maxm; i >= 0; i --) { //贪心输出最大
            if(dp[i] >= 1-maxp) {
                printf("%d\n", i); break;
            }
        }
    }
    return 0;
}

内容概要:论文提出了一种基于空间调制的能量高效分子通信方案(SM-MC),将传输符号分为空间符号和浓度符号。空间符号通过激活单个发射纳米机器人的索引来传输信息,浓度符号则采用传统的浓度移位键控(CSK)调制。相比现有的MIMO分子通信方案,SM-MC避免了链路间干扰,降低了检测复杂度并提高了性能。论文分析了SM-MC及其特例SSK-MC的符号错误率(SER),并通过仿真验证了其性能优于传统的MIMO-MC和SISO-MC方案。此外,论文还探讨了分子通信领域的挑战、优势及相关研究工作,强调了空间维度作为新的信息自由度的重要性,并提出了未来的研究方向和技术挑战。 适合人群:具备一定通信理论基础,特别是对纳米通信和分子通信感兴趣的科研人员、研究生和工程师。 使用场景及目标:①理解分子通信中空间调制的工作原理及其优势;②掌握SM-MC系统的具体实现细节,包括发射、接收、检测算法及性能分析;③对比不同分子通信方案(如MIMO-MC、SISO-MC、SSK-MC)的性能差异;④探索分子通信在纳米网络中的应用前景。 其他说明:论文不仅提供了详细的理论分析和仿真验证,还给出了具体的代码实现,帮助读者更好地理解和复现实验结果。此外,论文还讨论了分子通信领域的标准化进展,以及未来可能的研究方向,如混合调制方案、自适应调制技术和纳米机器协作协议等。
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