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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 <stdio.h>
2#include <string.h>
3
4int strStr(char * haystack, char * needle) {
5 int m = strlen(haystack);
6 int n = strlen(needle);
7 if (n == 0) return 0;
8 for (int i = 0; i <= m - n; i++) {
9 int j;
10 for (j = 0; j < n; j++) {
11 if (haystack[i + j] != needle[j])
12 break;
13 }
14 if (j == n) return i;
15 }
16 return -1;
17}
18
19int main() {
20 char *haystack = "sadbutsad";
21 char *needle = "sad";
22 printf("%d\n", strStr(haystack, needle)); // Output: 0
23 return 0;
24}This C code uses a naive approach to find the first occurrence of the needle in the haystack. It iterates over possible starting indices, checking if a matching substring can be found.
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
The Python code implements the KMP algorithm, using an LPS array to efficiently find the needle in the haystack.