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.
Analysis:
Use two map to store the expected char occured numbers(from T), realtime char occured numbers(From S)
Since we should implement this with O(n), we scan string S from the start to the end, use appeared varablile to recored how many chars have occued compared with T
If the (char is in T && its occued numbers < T), store it and its occued numbers in expectedMap, appeared++
If(appeared == T.lenth() )// substring could cover all chars in T
while(starting char is not in T || starting char numbers > expected number in T)
starting index++
if(win_len> current window length)
set new window
return ....
C++实现如下
string minWindow(string S, string T) {
if(S.size() == 0) return "";
if(T.size() > S.size()) return "";
int appearCount[256];
int expectCount[256];
memset(appearCount,0,256*sizeof(appearCount[0]));
memset(expectCount,0,256*sizeof(appearCount[0]));
for(int i=0; i<T.size();i++){
expectCount[T[i]]++;
}
int minV = INT_MAX, min_start=0;
int wid_start =0;
int appeared = 0;
for(int wid_end = 0; wid_end<S.size(); wid_end++){
if(expectCount[S[wid_end]] >0)
{
appearCount[S[wid_end]]++;
if(appearCount[S[wid_end]] <= expectCount[S[wid_end]])
appeared++;
}
if(appeared == T.size()){
while((appearCount[S[wid_start]]>expectCount[S[wid_start]]) || expectCount[S[wid_start]]==0)
{
appearCount[S[wid_start]]--;
wid_start++;
}
if(minV > (wid_end-wid_start+1))
{
minV = wid_end-wid_start+1;
min_start = wid_start;
}
}
}
if(minV == INT_MAX) return"";
return S.substr(min_start,minV);
}Java实现如下
public String minWindow(String S, String T) {
int win_start = 0;
int win_realS = 0;
int win_len = Integer.MAX_VALUE;
int appeared = 0;
HashMap<Character, Integer> expected = new HashMap<Character, Integer>();
HashMap<Character, Integer> realAppeared = new HashMap<Character, Integer>();
for(int i=0;i<T.length();i++){//store T char into map
int val = 1;
if(expected.containsKey(T.charAt(i))){
val = expected.get(T.charAt(i));
val++;
}
expected.put(T.charAt(i), val);
}
for(int i=0;i<S.length();i++){
if(expected.containsKey(S.charAt(i))){
int val = 0;
if(realAppeared.containsKey(S.charAt(i))){
val = realAppeared.get(S.charAt(i));
val++;
}else{
val = 1;
}
realAppeared.put(S.charAt(i), val);
if(val<=expected.get(S.charAt(i)))
appeared++;
}
if(appeared == T.length()){//have all the chars
while(!expected.containsKey(S.charAt(win_start))
|| (expected.containsKey(S.charAt(win_start)) && realAppeared.get(S.charAt(win_start))>expected.get(S.charAt(win_start)))){
if(!expected.containsKey(S.charAt(win_start)))
win_start++;
else{
int val = realAppeared.get(S.charAt(win_start));
val--;
realAppeared.put(S.charAt(win_start), val);
win_start++;
}
}
if(win_len>i-win_start+1){
win_len = i-win_start+1;
win_realS = win_start;
}
}
}
return (win_len == Integer.MAX_VALUE) ? "" : S.substring(win_realS, win_realS+win_len);
}

本文介绍了一种在字符串S中寻找包含字符串T所有字符的最短子串的算法,复杂度为O(n)。通过使用两个映射表来跟踪期望出现的字符数量与实际出现的字符数量,并结合双指针技巧,实现了高效查找。
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