经典DP水题D

本文介绍了一种算法挑战,即通过给定的一系列参数构建最高的堆叠游戏块。使用二维数组来记录每种可能的堆叠组合,并采用动态规划的方法来找出可以达到的最大高度。

Description

Michael The Kid receives an interesting game set from his grandparent as his birthday gift. Inside the game set box, there are n tiling blocks and each block has a form as follows: 

Each tiling block is associated with two parameters (l,m), meaning that the upper face of the block is packed with l protruding knobs on the left and m protruding knobs on the middle. Correspondingly, the bottom face of an (l,m)-block is carved with l caving dens on the left and m dens on the middle. 
It is easily seen that an (l,m)-block can be tiled upon another (l,m)-block. However,this is not the only way for us to tile up the blocks. Actually, an (l,m)-block can be tiled upon another (l',m')-block if and only if l >= l' and m >= m'. 
Now the puzzle that Michael wants to solve is to decide what is the tallest tiling blocks he can make out of the given n blocks within his game box. In other words, you are given a collection of n blocks B = {b1, b2, . . . , bn} and each block bi is associated with two parameters (li,mi). The objective of the problem is to decide the number of tallest tiling blocks made from B. 

Input

Several sets of tiling blocks. The inputs are just a list of integers.For each set of tiling blocks, the first integer n represents the number of blocks within the game box. Following n, there will be n lines specifying parameters of blocks in B; each line contains exactly two integers, representing left and middle parameters of the i-th block, namely, li and mi. In other words, a game box is just a collection of n blocks B = {b1, b2, . . . , bn} and each block bi is associated with two parameters (li,mi). 
Note that n can be as large as 10000 and li and mi are in the range from 1 to 100. 
An integer n = 0 (zero) signifies the end of input.

Output

For each set of tiling blocks B, output the number of the tallest tiling blocks can be made out of B. Output a single star '*' to signify the end of 
outputs.

Sample Input

3
3 2
1 1
2 3
5
4 2
2 4
3 3
1 1
5 5
0

Sample Output

2
3
*


题意:两串序列同时要最长上升子序列。

思路:一开始直接排序一个然后DP另一个。。。果断WA。。。

new一个a[][]和dp[][],a[i][j]++,画个表出来就懂了。

关键代码:dp[i][j] = Math.max(dp[i - 1][j], dp[i][j - 1]) + a[i][j];(参考网上。。为啥自己就想不出来嘞。。)


AC java 代码:


import java.util.Scanner;


public class sdupractice0724DPD {
//5324 KB 360 ms
public static void main(String[] args) {
Scanner scan = new Scanner(System.in);
int n = scan.nextInt();
while (n != 0) {
int[][] a = new int[105][105];
int[][] dp = new int[105][105];
for (int i = 1; i <= n; i++) {
int c = scan.nextInt();
int b = scan.nextInt();
a[c][b]++;
}
for (int i = 1; i <= 100; i++) {
for (int j = 1; j <= 100; j++) {
dp[i][j] = Math.max(dp[i - 1][j], dp[i][j - 1]) + a[i][j];
}
}
System.out.println(dp[100][100]);
n = scan.nextInt();
}
System.out.println("*");
}
}






根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结(summarization) 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签,可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
### Bitmask 动态规划练习及其解法 Bitmask 动态规划是一种结合了位运算和动态规划的思想方法,适用于状态数量有限但复杂度较高的问。以下是几个经典的 bitmask DP及其实现思路。 --- #### **经典目 1**: LeetCode 698. Partition to K Equal Sum Subsets 给定一个整数数组 `nums` 和正整数 `k`,判断能否将这个数组分成 `k` 个子集,使得每个子集的和相同。 ##### 解法分析 该问可以通过 bitmask 来表示当前已经分配到哪些元素的状态。定义 `dp[mask]` 表示是否能够通过某些组合形成目标和的部分集合。初始条件为 `dp[0] = true`,即没有任何元素被选中的情况下是可以满足条件的[^2]。 代码实现如下: ```java public boolean canPartitionKSubsets(int[] nums, int k) { int totalSum = Arrays.stream(nums).sum(); if (totalSum % k != 0 || k > nums.length) return false; int target = totalSum / k; int n = nums.length; boolean[] dp = new boolean[1 << n]; dp[0] = true; int[] sums = new int[1 << n]; // 记录每种状态下已有的总和 for (int mask = 0; mask < (1 << n); ++mask) { if (!dp[mask]) continue; for (int i = 0; i < n; ++i) { if (((mask >> i) & 1) == 0 && sums[mask] + nums[i] <= target) { int nextMask = mask | (1 << i); if (!dp[nextMask]) { dp[nextMask] = true; sums[nextMask] = (sums[mask] + nums[i]) % target; if (nextMask == (1 << n) - 1) return true; } } } } return dp[(1 << n) - 1]; } ``` --- #### **经典目 2**: Codeforces Problem C. Maximum XOR Subset 找到一个大小不超过 `m` 的子集的最大异或值。 ##### 解法分析 利用 bitmask 枚举所有可能的子集,并计算其对应的异或值。由于最多只有 \(O(2^n)\) 种可能性,在合理范围内可以直接枚举并记录最大值[^3]。 代码实现如下: ```cpp #include <bits/stdc++.h> using namespace std; int main() { int n, m; cin >> n >> m; vector<int> a(n); for(auto& x : a) cin>>x; int max_xor = 0; for(int mask=0; mask<(1<<n); ++mask){ if(__builtin_popcount(mask)>m) continue; // 跳过超过限制的情况 int current_xor = 0; for(int j=0; j<n; ++j){ if((mask&(1<<j))!=0){ current_xor ^=a[j]; } } max_xor = max(max_xor, current_xor); } cout << max_xor; } ``` --- #### **经典目 3**: AtCoder ABC D-Level Problems (e.g., Traveling Salesman Problem with Constraints) 旅行商问变体:在一个图中访问若干节点一次,返回最短路径长度。 ##### 解法分析 使用 bitmask 存储当前访问过的城市集合,转移方程为: \[ dp[mask][u] = \min(dp[mask'][v] + dist[v][u]) \] 其中 `mask'` 是去掉当前城市的前缀状态,`dist[v][u]` 表示从城市 `v` 到城市 `u` 的距离[^4]。 代码框架如下: ```python from functools import lru_cache def tsp(graph): N = len(graph) INF = float('inf') @lru_cache(None) def dfs(mask, u): # 当前状态和当前位置 if mask == (1 << N) - 1 and u == 0: # 所有城市都访问过了且回到了起点 return graph[u][0] or INF min_cost = INF for v in range(N): if not (mask & (1 << v)): # 如果未访问过此城市 cost = graph[u][v] or INF min_cost = min(min_cost, cost + dfs(mask | (1 << v), v)) return min_cost return dfs(1, 0) # Example usage: graph = [ [0, 10, 15, 20], [10, 0, 35, 25], [15, 35, 0, 30], [20, 25, 30, 0] ] print(tsp(graph)) ``` --- ###
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