How do you solve Find the Longest Semi-Repetitive Substring on LeetCode?

Find the Longest Semi-Repetitive Substring (LeetCode #2730) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(1) space complexity. Key insight: Understanding the definition of semi-repetitive is crucial to solving the problem correctly.

What is the brute force solution for Find the Longest Semi-Repetitive Substring?

The brute force approach for Find the Longest Semi-Repetitive Substring is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves checking every possible substring of the input string to see if it is semi-repetitive. This is straightforward but inefficient for larger strings. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Find the Longest Semi-Repetitive Substring?

Find the Longest Semi-Repetitive Substring uses the following concepts: String, Sliding Window. The recommended patterns to study are: Sliding Window, Two Pointers.

What are the interview tips for Find the Longest Semi-Repetitive Substring?

Always clarify the problem statement and constraints before jumping into coding. Think about the efficiency of your solution and be ready to explain your approach. Practice explaining your thought process while coding, as this is often part of the interview.

What are common mistakes when solving Find the Longest Semi-Repetitive Substring?

Failing to correctly count adjacent pairs, leading to incorrect results. Not considering edge cases, such as strings with all identical characters.

#2730

Find the Longest Semi-Repetitive Substring

Medium

StringSliding WindowSliding WindowTwo Pointers

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

Time O(n)Space O(1)

Using a sliding window approach allows us to efficiently find the longest semi-repetitive substring without generating all possible substrings. This method keeps track of the number of adjacent pairs as we expand and contract our window.

⚙️

Algorithm

4 steps

1Step 1: Initialize two pointers (left and right) to represent the current window and a variable to count adjacent pairs.
2Step 2: Expand the right pointer to include new characters in the window, updating the count of adjacent pairs.
3Step 3: If the count of adjacent pairs exceeds 1, move the left pointer to reduce the size of the window until the count is valid again.
4Step 4: Keep track of the maximum length of valid windows during the process.

solution.py20 lines

1# Full working Python code
2
3def longest_semi_repetitive_substring(s):
4    left = 0
5    max_length = 0
6    adjacent_count = 0
7    n = len(s)
8
9    for right in range(n):
10        if right > 0 and s[right] == s[right - 1]:
11            adjacent_count += 1
12        while adjacent_count > 1:
13            if s[left] == s[left + 1]:
14                adjacent_count -= 1
15            left += 1
16        max_length = max(max_length, right - left + 1)
17    return max_length
18
19# Example usage
20print(longest_semi_repetitive_substring('52233'))

ℹ

Complexity note: The time complexity is O(n) because we only traverse the string once with the two pointers. The space complexity is O(1) since we are using a fixed amount of extra space.

1Understanding the definition of semi-repetitive is crucial to solving the problem correctly.
2Using a sliding window technique can significantly optimize the solution by avoiding unnecessary checks.

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