How do you solve Shortest Matching Substring on LeetCode?

Shortest Matching Substring (LeetCode #3455) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Understanding the role of '*' in pattern matching is crucial.

What is the time complexity of Shortest Matching Substring?

The optimal solution for Shortest Matching Substring runs in O(n) time and uses O(n) space. KMP allows us to find occurrences efficiently, leading to O(n) time complexity. Space complexity is O(n) due to storing indices.

What is the brute force solution for Shortest Matching Substring?

The brute force approach for Shortest Matching Substring is Brute Force, with O(n²) time complexity and O(1) space complexity. We can check every possible substring of `s` to see if it matches the pattern `p`. This involves generating all substrings and validating them against `p`. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Shortest Matching Substring?

Shortest Matching Substring uses the following concepts: Two Pointers, String, Binary Search, String Matching. The recommended patterns to study are: KMP Algorithm, Two Pointers.

What are the interview tips for Shortest Matching Substring?

Clarify the problem and constraints before diving into coding. Discuss your thought process and approach with the interviewer. Consider edge cases and test your solution thoroughly.

What are common mistakes when solving Shortest Matching Substring?

Not considering the empty substring as a valid match. Failing to handle cases where the pattern does not match any part of the string.

#3455

Shortest Matching Substring

Hard

Two PointersStringBinary SearchString MatchingKMP AlgorithmTwo Pointers

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

Split the pattern into three parts around the '*' characters. Use KMP to find occurrences of the first and last parts, then check the middle part for minimal matching.

⚙️

Algorithm

3 steps

1Step 1: Split pattern `p` into three parts: `pre`, `mid`, `post` based on '*' positions.
2Step 2: Use KMP to find all starting indices of `pre` in `s` and all ending indices of `post` in `s`.
3Step 3: For each combination of start and end indices, check if `mid` can fit between them and update the minimum length.

solution.py14 lines

1def shortest_matching_substring(s, p):
2    pre, mid, post = p.split('*')
3    starts = kmp_search(s, pre)
4    ends = kmp_search(s, post)
5    min_length = float('inf')
6    for start in starts:
7        for end in ends:
8            if start < end:
9                min_length = min(min_length, end - start + len(pre) + len(post))
10    return min_length if min_length != float('inf') else -1
11
12def kmp_search(s, pattern):
13    # KMP implementation to find occurrences
14    return []

ℹ

Complexity note: KMP allows us to find occurrences efficiently, leading to O(n) time complexity. Space complexity is O(n) due to storing indices.

1Understanding the role of '*' in pattern matching is crucial.
2Using efficient searching algorithms like KMP can significantly reduce time complexity.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.