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 emtpy string

`""`

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

**Understand the problem:**

The problem asks for finding the minimum window substring. Note that the time complexity should be in O(n) time.

**Naive Approach:**

A very straight-forward solution could be save the characters of T into a hashset, then iterate though the string from beginning. When found an existed character at the hashset, we delete that one until the hashset is empty, which means we found the string that contains all the characters in T. Note that the T string could contain duplicated characters, so we must use a hash map to save its number of occurrence. If the number of occurrence equals to 1, we delete that key.

For this algorithm, it will take O(n^2) time because we found all substrings that fulfill the requirements, of each times O(n) time.

**Code (Java):**

public class Solution { public String minWindow(String S, String T) { if (S == null || S.length() == 0 || T == null || T.length() == 0) { return ""; } HashMap<Character, Integer> hashMap = new HashMap<Character, Integer>(); for (int i = 0; i < T.length(); i++) { if (hashMap.containsKey(T.charAt(i))) { int freq= hashMap.get(T.charAt(i)) + 1; hashMap.put(T.charAt(i), freq); } else { hashMap.put(T.charAt(i), 1); } } int start = 0; int min = Integer.MAX_VALUE; String result = ""; while (start < S.length()) { HashMap<Character, Integer> currMap = new HashMap<Character, Integer>(hashMap); int end = start; while (end < S.length() && !currMap.isEmpty()) { if (currMap.containsKey(S.charAt(end))) { if (currMap.get(S.charAt(end)) == 1) { currMap.remove(S.charAt(end)); } else { int freq = currMap.get(S.charAt(end)); currMap.put(S.charAt(end), freq - 1); } } end++; } if (currMap.isEmpty() && (end - start < min)) { min = end - start; result = S.substring(start, end); } start++; } return result; } }

Not surprisingly, it got the TLE error. So we need to find out a linear time solution.

**A better Solution:**

The idea of the linear solution is first to find out a substring window that contains all characters of the T string. Then we shrink the window by moving forward of the start pointer. The rule is we can move on the start point if the character at start is not in the hash map, or the hash map still contains a full set of target characters even after we remove it.

**Code (Java):**

public class Solution { public String minWindow(String S, String T) { if (S == null || S.length() == 0 || T == null || T.length() == 0) { return ""; } HashMap<Character, Integer> map = new HashMap<Character, Integer>(); HashMap<Character, Integer> dict = new HashMap<Character, Integer>(); for (int i = 0; i < T.length(); i++) { map.put(T.charAt(i), 0); if (dict.containsKey(T.charAt(i))) { dict.put(T.charAt(i), dict.get(T.charAt(i)) + 1); } else { dict.put(T.charAt(i), 1); } } int start = 0; int count = 0; int minLen = S.length() + 1; String result = ""; for (int end = 0; end < S.length(); end++) { if (map.containsKey(S.charAt(end))) { map.put(S.charAt(end), map.get(S.charAt(end)) + 1); if (map.get(S.charAt(end)) <= dict.get(S.charAt(end))) { count++; } } if (count == T.length()) { while (!dict.containsKey(S.charAt(start)) || map.get(S.charAt(start)) > dict.get(S.charAt(start))) { if (map.containsKey(S.charAt(start))) { map.put(S.charAt(start), map.get(S.charAt(start)) -1); } start++; } if (end - start + 1 < minLen) { minLen = end - start + 1; result = S.substring(start, end + 1); } } } return result; } }

**Discussion:**

Note that we used two hash map because there might be duplicated characters in the T string. So the initial number of occurrence for each character may not be 1. Therefore when we determine the count, which is to decide if the current window contains all characters in the T, we cannot simply check each character occurred more than once.

**Summary:**

This is another dictionary related question. For this kind of question, it is naturally to use hash Map.

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