76 - Minimum Window Substring

本文介绍了一种复杂度为O(n)的算法,用于在给定字符串中找到最短的子串,该子串包含另一个字符串的所有字符。通过逐步扫描并调整窗口大小,实现算法效率最大化。

Given a string S and a string T, find the minimum window in S which will contain all the characters in T in complexity O(n).

For example,
S = "ADOBECODEBANC"
T = "ABC"

Minimum window is "BANC".

Note:
If there is no such window in S that covers all characters in T, return the empty string "".

If there are multiple such windows, you are guaranteed that there will always be only one unique minimum window in S.

思路分析:

此题较难。

1、初始化 start = i = 0

2、i 逐渐往后扫描S直到窗口S[start…i]包含所有T的字符,此时i = 6(字符c的位置)

3、缩减窗口:此时我们注意到窗口并不是最小的,需要调整 start 来缩减窗口。缩减规则为:如果S[start]不在T中 或者 S[start]在T中但是删除后窗口依然可以包含T中的所有字符,那么start = start+1, 直到不满足上述两个缩减规则。缩减后i即为窗口的起始位置,此例中从e开始窗口中要依次删掉e、b、a、d,start最后等于4 ,那么得到一个窗口大小为6-4+1 = 3

4、start = start+1(此时窗口肯定不会包含所有的T中的字符),跳转到步骤2继续寻找下一个窗口。本例中还以找到一个窗口start = 5,i = 8,比上个窗口大,因此最终的最小窗口是S[4…6]


/*
*/

#include "stdafx.h"
#include <iostream>

using namespace std;

class Solution_076_MinimumWindowSubstring_1 
{
private:
	int count1[256];
	int count2[256];
public:
	string minWindow(string S, string T) 
	{
		// Start typing your C/C++ solution below
		// DO NOT write int main() function
		if (T.size() == 0 || S.size() == 0)
			return "";

		memset(count1, 0, sizeof(count1));
		memset(count2, 0, sizeof(count2));

		for (int i = 0; i < T.size(); i++)
		{
			count1[T[i]]++;
			count2[T[i]]++;
		}

		int count = T.size();

		int start = 0;
		int minSize = INT_MAX;
		int minStart;
		for (int end = 0; end < S.size(); end++)
		{
			if (count2[S[end]] > 0)
			{
				count1[S[end]]--;
				if (count1[S[end]] >= 0)
					count--;
			}

			if (count == 0)
			{
				while (true)
				{
					if (count2[S[start]] > 0)
					{
						if (count1[S[start]] < 0)
							count1[S[start]]++;
						else
							break;
					}
					start++;
				}

				if (minSize > end - start + 1)
				{
					minSize = end - start + 1;
					minStart = start;
				}
			}
		}

		if (minSize == INT_MAX)
			return "";

		string ret(S, minStart, minSize);

		return ret;
	}
};



To solve this problem, we can use the sliding window approach again. Here's the algorithm: 1. Initialize two dictionaries: need and window. need stores the count of each character in t, and window stores the count of each character in the current window. 2. Initialize two pointers left and right to mark the current window, and two variables match and required to track the number of matched characters and the number of required characters respectively. 3. Initialize a variable min_len to a large value and a variable start to 0 to store the start index of the minimum window substring. 4. While the right pointer is less than the length of the string s: - If the character at s[right] is in need, add it to window and update match and required accordingly. - While all characters in need are included in window, update min_len and start accordingly, and remove the character at s[left] from window and update match and required accordingly. - Move the left pointer to the right. - Move the right pointer to the right. 5. Return the minimum window substring starting from index start and having length min_len, or the empty string if no such substring exists. Here's the Python code for the algorithm: ``` def min_window(s, t): need = {} for c in t: need[c] = need.get(c, 0) + 1 window = {} left = right = 0 match = 0 required = len(need) min_len = float('inf') start = 0 while right < len(s): if s[right] in need: window[s[right]] = window.get(s[right], 0) + 1 if window[s[right]] == need[s[right]]: match += 1 while match == required: if right - left + 1 < min_len: min_len = right - left + 1 start = left if s[left] in need: window[s[left]] -= 1 if window[s[left]] < need[s[left]]: match -= 1 left += 1 right += 1 return s[start:start+min_len] if min_len != float('inf') else "" ``` Example usage: ``` s = "ADOBECODEBANC" t = "ABC" print(min_window(s, t)) # Output: "BANC" ```
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