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The brute force approach involves iterating over each possible starting position of the needle in the haystack and checking if the substring from that position matches the needle.
Time Complexity: O((m-n+1)*n), where m is the length of haystack and n is the length of needle.
Space Complexity: O(1).
1def strStr(haystack: str, needle: str) -> int:
2 m, n = len(haystack), len(needle)
3 if n == 0:
4 return 0
5 for i in range(m - n + 1):
6 if haystack[i:i+n] == needle:
7 return i
8 return -1
9
10print(strStr("sadbutsad", "sad")) # Output: 0This Python solution uses slicing to compare parts of the haystack with the needle.
The KMP (Knuth-Morris-Pratt) algorithm is a more efficient string-searching algorithm. It preprocesses the needle to create a longest prefix-suffix (LPS) array, which is used to skip unnecessary comparisons while searching in the haystack.
Time Complexity: O(m + n), where m is the length of haystack and n is the length of needle.
Space Complexity: O(n), due to the LPS array.
1#include <vector>
using namespace std;
void computeLPSArray(string pat, int M, int* lps) {
int len = 0;
lps[0] = 0;
int i = 1;
while (i < M) {
if (pat[i] == pat[len]) {
len++;
lps[i] = len;
i++;
} else {
if (len != 0) {
len = lps[len - 1];
} else {
lps[i] = 0;
i++;
}
}
}
}
int strStr(string txt, string pat) {
int N = txt.size();
int M = pat.size();
int lps[M];
computeLPSArray(pat, M, lps);
int i = 0;
int j = 0;
while (i < N) {
if (pat[j] == txt[i]) {
j++;
i++;
}
if (j == M) {
return i - j;
j = lps[j - 1];
} else if (i < N && pat[j] != txt[i]) {
if (j != 0)
j = lps[j - 1];
else
i = i + 1;
}
}
return -1;
}
int main() {
cout << strStr("sadbutsad", "sad") << endl; // Output: 0
return 0;
}This C++ implementation follows the KMP algorithm for efficient string searching.