How do you solve Maximize the Confusion of an Exam on LeetCode?

Maximize the Confusion of an Exam (LeetCode #2024) 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 helps efficiently manage the number of changes needed.

What is the time complexity of Maximize the Confusion of an Exam?

The optimal solution for Maximize the Confusion of an Exam runs in O(n) time and uses O(1) space. This complexity is linear because we only traverse the string once with two pointers, adjusting the window size dynamically.

What is the brute force solution for Maximize the Confusion of an Exam?

The brute force approach for Maximize the Confusion of an Exam is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves checking every possible substring of the answer key and counting the maximum consecutive characters after changing up to k characters. This is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Maximize the Confusion of an Exam?

Maximize the Confusion of an Exam uses the following concepts: String, Binary Search, Sliding Window, Prefix Sum. The recommended patterns to study are: Sliding Window, Two Pointers.

What are the interview tips for Maximize the Confusion of an Exam?

Always consider edge cases, such as when k is larger than the length of the string. Explain your thought process clearly while coding, especially when using pointers. Practice identifying patterns in problems, as they often lead to more efficient solutions.

What are common mistakes when solving Maximize the Confusion of an Exam?

Not correctly managing the left pointer in the sliding window approach. Overcomplicating the brute force solution by not considering substrings efficiently.

#2024

Maximize the Confusion of an Exam

Medium

StringBinary SearchSliding WindowPrefix SumSliding WindowTwo Pointers

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

Time O(n)Space O(1)

We can use a sliding window approach to maintain a window of characters that can be converted with at most k changes. This allows us to efficiently find the maximum length of consecutive characters.

⚙️

Algorithm

4 steps

1Step 1: Initialize two pointers (left and right) and a count for changes.
2Step 2: Expand the right pointer to include more characters until the number of changes exceeds k.
3Step 3: If changes exceed k, move the left pointer to reduce the window size until changes are within k.
4Step 4: Keep track of the maximum length of the window during this process.

solution.py18 lines

1def maxConsecutiveAnswers(answerKey, k):
2    left = 0
3    max_length = 0
4    count_T = 0
5    count_F = 0
6    for right in range(len(answerKey)):
7        if answerKey[right] == 'T':
8            count_T += 1
9        else:
10            count_F += 1
11        while min(count_T, count_F) > k:
12            if answerKey[left] == 'T':
13                count_T -= 1
14            else:
15                count_F -= 1
16            left += 1
17        max_length = max(max_length, right - left + 1)
18    return max_length

ℹ

Complexity note: This complexity is linear because we only traverse the string once with two pointers, adjusting the window size dynamically.

1Using a sliding window helps efficiently manage the number of changes needed.
2The problem can be viewed as finding the longest segment of a string with at most k changes.

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