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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).
This JavaScript solution examines each substring of the haystack to check for a match 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.
1def strStr(haystack: str, needle: str) -> int:
2 if not needle:
3 return 0
4 def computeLPS(pattern):
5 lps = [0] * len(pattern)
6 length = 0
7 i = 1
8 while i < len(pattern):
9 if pattern[i] == pattern[length]:
10 length += 1
11 lps[i] = length
12 i += 1
13 else:
14 if length != 0:
15 length = lps[length - 1]
16 else:
17 lps[i] = 0
18 i += 1
19 return lps
20 lps = computeLPS(needle)
21 i = j = 0
22 while i < len(haystack):
23 if haystack[i] == needle[j]:
24 i += 1
25 j += 1
26 if j == len(needle):
27 return i - j
28 elif i < len(haystack) and haystack[i] != needle[j]:
29 if j != 0:
30 j = lps[j - 1]
31 else:
32 i += 1
33 return -1
34
35print(strStr("sadbutsad", "sad")) # Output: 0The Python code implements the KMP algorithm, using an LPS array to efficiently find the needle in the haystack.