HDU-1306-String Matching

String Matching

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/32768 K (Java/Others)
Total Submission(s): 776    Accepted Submission(s): 404


Problem Description
It's easy to tell if two words are identical - just check the letters. But how do you tell if two words are almost identical? And how close is "almost"?

There are lots of techniques for approximate word matching. One is to determine the best substring match, which is the number of common letters when the words are compared letter-byletter.

The key to this approach is that the words can overlap in any way. For example, consider the words CAPILLARY and MARSUPIAL. One way to compare them is to overlay them: 

CAPILLARY
MARSUPIAL

There is only one common letter (A). Better is the following overlay:

CAPILLARY
   MARSUPIAL

with two common letters (A and R), but the best is:

  CAPILLARY
MARSUPIAL 

Which has three common letters (P, I and L).

The approximation measure appx(word1, word2) for two words is given by:

common letters * 2
-----------------------------
length(word1) + length(word2)

Thus, for this example, appx(CAPILLARY, MARSUPIAL) = 6 / (9 + 9) = 1/3. Obviously, for any word W appx(W, W) = 1, which is a nice property, while words with no common letters have an appx value of 0.
 

Sample Input
The input for your program will be a series of words, two per line, until the end-of-file flag of -1. Using the above technique, you are to calculate appx() for the pair of words on the line and print the result. For example: CAR CART TURKEY CHICKEN MONEY POVERTY ROUGH PESKY A A -1 The words will all be uppercase.
 

Sample Output
Print the value for appx() for each pair as a reduced fraction, like this: appx(CAR,CART) = 6/7 appx(TURKEY,CHICKEN) = 4/13 appx(MONEY,POVERTY) = 1/3 appx(ROUGH,PESKY) = 0 appx(A,A) = 1
 

Recommend
Eddy
 
import java.util.Scanner;

public class String_Matching_1306_201308121604 {
	public static void main(String[] args) {
		Scanner sc = new Scanner (System.in);	
		while(sc.hasNext()){//8919883 2013-08-12 18:34:54 Accepted 1306	109MS	2848K	1342 B	Java	1983210400
			String st = sc.next();
			if(st.equalsIgnoreCase("-1"))
				break;
			String sr = sc.next();
			int stlen = st.length();
			int srlen = sr.length();
           
			int count,max=0,i,j;
			for( i=0; i<stlen; i++){
				 count=0;
				 int h=i;
				for( j=0; j<srlen && h<stlen; ){//正面匹配
					char st1 = st.charAt(h++);
					char sr1 = sr.charAt(j++);
					if(st1==sr1)
						count++;						
				}
				 if(max<count)
				     max=count;
				
			}
			
			for( j=0; j<srlen; j++){
				 count=0;
				 int h=j;
				for(i=0; i<stlen&& h<srlen; ){//反面匹配
					char st1 = st.charAt(i++);
					char sr1 = sr.charAt(h++);
					if(st1==sr1)
						count++;						
				}
				 if(max<count)
				     max=count;
				
			}
			int ss = stlen+srlen;
			if(max==0)
				System.out.println("appx"+"("+st+","+sr+") = "+0);
			else{
				
				if(ss%2*max==0){
					int ss1 = ss/(2*max);
					if(ss1!=1)
						System.out.println("appx"+"("+st+","+sr+") = "+1+"/"+ss1);
					else
						System.out.println("appx"+"("+st+","+sr+") = "+1);
				}		
					
				else{
						System.out.println("appx"+"("+st+","+sr+") = "+2*max+"/"+ss);
				}
			}
			
		}
	}
   
}


HDU-3480 是一个典型的动态规划问题,其题目标题通常为 *Division*,主要涉及二维费用背包问题或优化后的动态规划策略。题目大意是:给定一个整数数组,将其划分为若干个连续的子集,每个子集最多包含 $ m $ 个元素,并且每个子集的最大值与最小值之差不能超过给定的阈值 $ t $,目标是使所有子集的划分代价总和最小。每个子集的代价是该子集最大值与最小值的差值。 ### 动态规划思路 设 $ dp[i] $ 表示前 $ i $ 个元素的最小代价。状态转移方程如下: $$ dp[i] = \min_{j=0}^{i-1} \left( dp[j] + cost(j+1, i) \right) $$ 其中 $ cost(j+1, i) $ 表示从第 $ j+1 $ 到第 $ i $ 个元素构成一个子集的代价,即 $ \max(a[j+1..i]) - \min(a[j+1..i]) $。 为了高效计算 $ cost(j+1, i) $,可以使用滑动窗口或单调队列等数据结构来维护区间最大值与最小值,从而将时间复杂度优化到可接受的范围。 ### 示例代码 以下是一个简化版本的动态规划实现,使用暴力方式计算区间代价,适用于理解问题结构: ```cpp #include <bits/stdc++.h> using namespace std; const int INF = 0x3f3f3f3f; const int MAXN = 10010; int a[MAXN]; int dp[MAXN]; int main() { int T, n, m; cin >> T; for (int Case = 1; Case <= T; ++Case) { cin >> n >> m; for (int i = 1; i <= n; ++i) cin >> a[i]; dp[0] = 0; for (int i = 1; i <= n; ++i) { dp[i] = INF; int mn = a[i], mx = a[i]; for (int j = i; j >= max(1, i - m + 1); --j) { mn = min(mn, a[j]); mx = max(mx, a[j]); if (mx - mn <= T) { dp[i] = min(dp[i], dp[j - 1] + mx - mn); } } } cout << "Case " << Case << ": " << dp[n] << endl; } return 0; } ``` ### 优化策略 - **单调队列**:可以使用两个单调队列分别维护当前窗口的最大值与最小值,从而将区间代价计算的时间复杂度从 $ O(n^2) $ 降低到 $ O(n) $。 - **斜率优化**:若问题满足特定的决策单调性,可以考虑使用斜率优化技巧进一步加速状态转移过程。 ### 时间复杂度分析 原始暴力解法的时间复杂度为 $ O(n^2) $,在 $ n \leq 10^4 $ 的情况下可能勉强通过。通过单调队列优化后,可以稳定运行于 $ O(n) $ 或 $ O(n \log n) $。 ### 应用场景 HDU-3480 的问题模型可以应用于资源调度、任务划分等场景,尤其适用于需要控制子集内部差异的问题,如图像分块压缩、数据分段处理等[^1]。 ---
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值