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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).
1#include <iostream>
2using namespace std;
3int strStr(string haystack, string needle) {
4 int m = haystack.size();
5 int n = needle.size();
6 if (n == 0) return 0;
7 for (int i = 0; i <= m - n; i++) {
8 int j = 0;
9 for (j = 0; j < n; j++) {
10 if (haystack[i + j] != needle[j])
11 break;
12 }
13 if (j == n) return i;
14 }
15 return -1;
16}
17
18int main() {
19 cout << strStr("sadbutsad", "sad") << endl; // Output: 0
20 return 0;
21}This C++ solution uses a similar approach as the C solution, iterating over the haystack to match 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
This JavaScript implementation efficiently searches for the needle using the KMP algorithm with an LPS array.