How do you solve Longest Repeating Character Replacement on LeetCode?

Longest Repeating Character Replacement (LeetCode #424) 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: Using a sliding window allows us to efficiently track the longest valid substring without generating all substrings.

What is the brute force solution for Longest Repeating Character Replacement?

The brute force approach for Longest Repeating Character Replacement is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach checks every possible substring of the input string to see if it can be transformed into a substring with all the same characters using at most k replacements. This method is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Longest Repeating Character Replacement?

Longest Repeating Character Replacement uses the following concepts: Hash Table, String, Sliding Window. The recommended patterns to study are: Sliding Window, Two Pointers, Hash Map.

What are the interview tips for Longest Repeating Character Replacement?

Always explain your thought process clearly when discussing your approach. Be prepared to optimize your solution if your initial approach is not efficient. Practice identifying patterns in problems, as many can be solved using similar techniques.

What are common mistakes when solving Longest Repeating Character Replacement?

Not updating the left pointer correctly when the window exceeds the allowed replacements. Forgetting to track the maximum frequency of characters in the current window.

#424

Longest Repeating Character Replacement

Medium

Hash TableStringSliding WindowSliding WindowTwo PointersHash Map

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)

The optimal solution uses a sliding window approach to efficiently find the longest substring that can be transformed into a repeating character substring with at most k replacements. This reduces the need to check every substring individually.

⚙️

Algorithm

5 steps

1Step 1: Initialize two pointers (left and right) to represent the current window and a variable to count the maximum frequency of any character in the current window.
2Step 2: Expand the right pointer to include more characters in the window.
3Step 3: Update the frequency of the current character and the maximum frequency found in the window.
4Step 4: If the current window size minus the maximum frequency is greater than k, move the left pointer to shrink the window until the condition is satisfied.
5Step 5: Update the maximum length of the valid window found during the process.

solution.py16 lines

1# Full working Python code
2
3def characterReplacement(s, k):
4    left = 0
5    count = [0] * 26
6    max_count = 0
7    max_length = 0
8    for right in range(len(s)):
9        count[ord(s[right]) - ord('A')] += 1
10        max_count = max(max_count, count[ord(s[right]) - ord('A')])
11        while (right - left + 1) - max_count > k:
12            count[ord(s[left]) - ord('A')] -= 1
13            left += 1
14        max_length = max(max_length, right - left + 1)
15    return max_length
16

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Complexity note: The time complexity is O(n) because we traverse the string with two pointers, and each character is processed at most twice. The space complexity is O(1) since we use a fixed-size array for character counts.

1Using a sliding window allows us to efficiently track the longest valid substring without generating all substrings.
2The maximum frequency of characters helps determine how many replacements are needed.

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