hdu1087 最大上升子序列模板

本文介绍了一种求解最大上升子序列之和的问题,并提供了一个具体的算法实现案例。该算法通过动态规划的方法来找到一组正整数序列中,符合上升条件的子序列的最大和。
Nowadays, a kind of chess game called “Super Jumping! Jumping! Jumping!” is very popular in HDU. Maybe you are a good boy, and know little about this game, so I introduce it to you now.



The game can be played by two or more than two players. It consists of a chessboard(棋盘)and some chessmen(棋子), and all chessmen are marked by a positive integer or “start” or “end”. The player starts from start-point and must jumps into end-point finally. In the course of jumping, the player will visit the chessmen in the path, but everyone must jumps from one chessman to another absolutely bigger (you can assume start-point is a minimum and end-point is a maximum.). And all players cannot go backwards. One jumping can go from a chessman to next, also can go across many chessmen, and even you can straightly get to end-point from start-point. Of course you get zero point in this situation. A player is a winner if and only if he can get a bigger score according to his jumping solution. Note that your score comes from the sum of value on the chessmen in you jumping path.
Your task is to output the maximum value according to the given chessmen list.

Input Input contains multiple test cases. Each test case is described in a line as follow:
N value_1 value_2 …value_N
It is guarantied that N is not more than 1000 and all value_i are in the range of 32-int.
A test case starting with 0 terminates the input and this test case is not to be processed.
Output For each case, print the maximum according to rules, and one line one case.
Sample Input
3 1 3 2
4 1 2 3 4
4 3 3 2 1
0
Sample Output
4
10
3

题意就是要求最大上升子序列的和,这个算法和最长上升子序列有点类似,条件都是a[i]>a[j]这就是限定上升条件,转移方程dp[i]=max(dp[i],dp[j]+a[i])方程的意思就是如果第i个接在第j个后面的最大值,dp[i]就是前i个的最大上升子序列和的值,再一直更新最大值就可以了。

#include <iostream>
#include <cstdio>
#include <algorithm>

using namespace std;
int a[100009];
int dp[100009];
int main()
{
    int i,j,k,n,maxx;
    while(~scanf("%d",&n))
    {
        if(n==0) break;
        for(i=0; i<=n-1; i++)
        {
            scanf("%d",&a[i]);
        }
        maxx=0;
        for(i=0; i<=n-1; i++)
        {
            dp[i]=a[i];
            for(j=0; j<=i-1; j++)
            {
                if(a[i]>a[j])
                dp[i]=max(dp[i],dp[j]+a[i]);
            }
            maxx=max(dp[i],maxx);
        }
        printf("%d\n",maxx);
    }
}



### HDU 1159 最长公共子序列 (LCS) 解题思路 #### 动态规划状态定义 对于两个字符串 `X` 和 `Y`,长度分别为 `n` 和 `m`。设 `dp[i][j]` 表示 `X[0...i-1]` 和 `Y[0...j-1]` 的最长公共子序列的长度。 当比较到第 `i` 个字符和第 `j` 个字符时: - 如果 `X[i-1]==Y[j-1]`,那么这两个字符可以加入之前的 LCS 中,则有 `dp[i][j]=dp[i-1][j-1]+1`[^3]。 - 否则,如果 `X[i-1]!=Y[j-1]`,那么需要考虑两种情况中的最大值:即舍弃 `X[i-1]` 或者舍弃 `Y[j-1]`,因此取两者较大者作为新的 LCS 长度,即 `dp[i][j]=max(dp[i-1][j], dp[i][j-1])`。 时间复杂度为 O(n*m),其中 n 是第一个字符串的长度而 m 是第二个字符串的长度。 #### 实现代码 以下是 Python 版本的具体实现方式: ```python def lcs_length(X, Y): # 初始化二维数组用于存储中间结果 m = len(X) n = len(Y) # 创建(m+1)x(n+1)大小的表格来保存子问题的结果 dp = [[0]*(n+1) for _ in range(m+1)] # 填充表项 for i in range(1, m+1): for j in range(1, n+1): if X[i-1] == Y[j-1]: dp[i][j] = dp[i-1][j-1] + 1 else: dp[i][j] = max(dp[i-1][j], dp[i][j-1]) return dp[m][n] # 测试数据输入部分可以根据具体题目调整 if __name__ == "__main__": while True: try: a = input().strip() b = input().strip() result = lcs_length(a,b) print(result) except EOFError: break ``` 此程序会读入多组测试案例直到遇到文件结束符(EOF)。每组案例由两行组成,分别代表要计算其 LCS 的两个字符串。最后输出的是它们之间最长公共子序列的长度。
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