HDU - 3506 Monkey Party

本文探讨了HDU-3506猴子派对问题,详细解释了如何通过动态规划算法来最小化猴子间相互介绍所需的时间,以加速派对开始。文章提供了完整的代码实现,并阐述了解题思路。

HDU - 3506 Monkey Party


问题描述:

Far away from our world, there is a banana forest. And many lovely monkeys live there. One day, SDH(Song Da Hou), who is the king of banana forest, decides to hold a big party to celebrate Crazy Bananas Day. But the little monkeys don’t know each other, so as the king, SDH must do something.
Now there are n monkeys sitting in a circle, and each monkey has a making friends time. Also, each monkey has two neighbor. SDH wants to introduce them to each other, and the rules are:
1.every time, he can only introduce one monkey and one of this monkey’s neighbor.
2.if he introduce A and B, then every monkey A already knows will know every monkey B already knows, and the total time for this introducing is the sum of the making friends time of all the monkeys A and B already knows;
3.each little monkey knows himself;
In order to begin the party and eat bananas as soon as possible, SDH want to know the mininal time he needs on introducing.


INPUT:

There is several test cases. In each case, the first line is n(1 ≤ n ≤ 1000), which is the number of monkeys. The next line contains n positive integers(less than 1000), means the making friends time(in order, the first one and the last one are neighbors). The input is end of file.


output:

For each case, you should print a line giving the mininal time SDH needs on introducing.


#include"stdio.h"
#include"string"
#include"algorithm"
using namespace std;
#define INF 99999999
int dp[2001][2001];
int s[2001][2001];
int mk[2001];
unsigned long long sum[2001] = { 0 };
int  solve(int n)
{
    for (int len = 1; len < n; ++len)
    {
        for (int i = 1; i + len <= 2*n; ++i)
        {
            int j = i + len; dp[i][j] = INF;
            for(int k=s[i][j-1];k<=s[i+1][j];k++)
            if (dp[i][k] + dp[k + 1][j] + sum[j] - sum[i - 1] < dp[i][j])
            {
                dp[i][j] = dp[i][k] + dp[k + 1][j] + sum[j] - sum[i - 1];
                s[i][j] = k;
            }
        }
    }
    int ans = INF;
    for (int i = 1; i <= n; ++i)
    {
        ans = min(ans, dp[i][i + n - 1]);
    }
    return ans;
}
int main()
{
    int n;
    while (scanf("%d", &n) != EOF)
    {
        for (int i = 1; i <= n; ++i)
        {
            scanf("%d", &mk[i]);
            mk[i + n] = mk[i];
        }
        for (int i = 1; i < 2 * n; ++i)
        {
            s[i][i] = i;
            dp[i][i] = 0;
            sum[i] = sum[i - 1] + mk[i];
        }
        printf("%d\n",solve(n));
    }
    return 0;
}
### 关于HDU - 6609 的题目解析 由于当前未提供具体关于 HDU - 6609 题目的详细描述,以下是基于一般算法竞赛题型可能涉及的内容进行推测和解答。 #### 可能的题目背景 假设该题目属于动态规划类问题(类似于多重背包问题),其核心在于优化资源分配或路径选择。此类问题通常会给出一组物品及其属性(如重量、价值等)以及约束条件(如容量限制)。目标是最优地选取某些物品使得满足特定的目标函数[^2]。 #### 动态转移方程设计 如果此题确实是一个变种的背包问题,则可以采用如下状态定义方法: 设 `dp[i][j]` 表示前 i 种物品,在某种条件下达到 j 值时的最大收益或者最小代价。对于每一种新加入考虑范围内的物体 k ,更新规则可能是这样的形式: ```python for i in range(n): for s in range(V, w[k]-1, -1): dp[s] = max(dp[s], dp[s-w[k]] + v[k]) ``` 这里需要注意边界情况处理以及初始化设置合理值来保证计算准确性。 另外还有一种可能性就是它涉及到组合数学方面知识或者是图论最短路等相关知识点。如果是后者的话那么就需要构建相应的邻接表表示图形结构并通过Dijkstra/Bellman-Ford/Floyd-Warshall等经典算法求解两点间距离等问题了[^4]。 最后按照输出格式要求打印结果字符串"Case #X: Y"[^3]。 #### 示例代码片段 下面展示了一个简单的伪代码框架用于解决上述提到类型的DP问题: ```python def solve(): t=int(input()) res=[] cas=1 while(t>0): n,k=list(map(int,input().split())) # Initialize your data structures here ans=find_min_unhappiness() # Implement function find_min_unhappiness() res.append(f'Case #{cas}: {round(ans)}') cas+=1 t-=1 print("\n".join(res)) solve() ```
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