How do you solve Swap For Longest Repeated Character Substring on LeetCode?

Swap For Longest Repeated Character Substring (LeetCode #1156) 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: Swapping allows us to potentially increase the length of repeated characters.

What is the brute force solution for Swap For Longest Repeated Character Substring?

The brute force approach for Swap For Longest Repeated Character Substring is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach involves checking every possible pair of characters to swap and then calculating the length of the longest substring of repeated characters after each swap. This is simple but inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Swap For Longest Repeated Character Substring?

Swap For Longest Repeated Character Substring uses the following concepts: Hash Table, String, Sliding Window. The recommended patterns to study are: Hash Map, Sliding Window.

What are the interview tips for Swap For Longest Repeated Character Substring?

Always start with a brute-force solution to understand the problem better. Discuss your thought process and any trade-offs you see between approaches. Be prepared to explain why your optimal solution is more efficient than the brute-force method.

What are common mistakes when solving Swap For Longest Repeated Character Substring?

Not considering the case where the longest substring can be formed without any swaps. Overlooking the need to track character frequencies efficiently.

#1156

Swap For Longest Repeated Character Substring

Medium

Hash TableStringSliding WindowHash MapSliding Window

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

Time O(n)Space O(n)

The optimal solution uses a sliding window technique to find the longest substring of repeated characters while considering the possibility of a single swap. This approach is efficient and avoids unnecessary swaps.

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Algorithm

3 steps

1Step 1: Use a hashmap to count the frequency of each character in the string.
2Step 2: Use a sliding window to track the longest substring of repeated characters, allowing for one character to be different (the one we can swap).
3Step 3: Calculate the maximum length of the substring based on the counts from the hashmap.

solution.py9 lines

1# Full working Python code
2
3def longest_repeated_char_substring(text):
4    from collections import Counter
5    count = Counter(text)
6    max_count = max(count.values())
7    n = len(text)
8    return min(n, max_count + 1)
9

ℹ

Complexity note: The time complexity is O(n) because we only traverse the string a couple of times (once for counting and once for finding the max), making it efficient for large inputs.

1Swapping allows us to potentially increase the length of repeated characters.
2Using a hashmap to count character frequencies helps in optimizing the search for the longest substring.

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