How do you solve Apply Operations to Maximize Frequency Score on LeetCode?

Apply Operations to Maximize Frequency Score (LeetCode #2968) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n log n) time complexity and O(1) space complexity. Key insight: Sorting helps group similar elements together, making it easier to calculate operations.

What is the time complexity of Apply Operations to Maximize Frequency Score?

The optimal solution for Apply Operations to Maximize Frequency Score runs in O(n log n) time and uses O(1) space. The sorting step takes O(n log n) time, and the sliding window traversal is O(n), making this approach efficient overall.

What is the brute force solution for Apply Operations to Maximize Frequency Score?

The brute force approach for Apply Operations to Maximize Frequency Score is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach tries to calculate the maximum frequency by checking all possible combinations of increasing or decreasing elements. This method is straightforward but inefficient for large arrays. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Apply Operations to Maximize Frequency Score?

Apply Operations to Maximize Frequency Score uses the following concepts: Array, Binary Search, Sliding Window, Sorting, Prefix Sum. The recommended patterns to study are: Sliding Window, Sorting.

What are the interview tips for Apply Operations to Maximize Frequency Score?

Always clarify the constraints and edge cases before diving into coding. Explain your thought process clearly while coding to show your understanding. Practice similar problems to get comfortable with sliding window and binary search techniques.

What are common mistakes when solving Apply Operations to Maximize Frequency Score?

Not considering edge cases where k is 0. Failing to sort the array before applying the sliding window technique.

#2968

Apply Operations to Maximize Frequency Score

Hard

ArrayBinary SearchSliding WindowSortingPrefix SumSliding WindowSorting

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

Time O(n log n)Space O(1)

The optimal approach involves sorting the array and using a sliding window technique to find the longest subarray where we can make all elements equal with at most k operations. This is efficient and leverages the sorted order to minimize operations.

⚙️

Algorithm

5 steps

1Step 1: Sort the array to group similar elements together.
2Step 2: Use two pointers (left and right) to create a sliding window over the sorted array.
3Step 3: Calculate the total operations needed to make all elements in the window equal to the rightmost element of the window.
4Step 4: If the operations exceed k, move the left pointer to reduce the window size.
5Step 5: Keep track of the maximum size of the window that meets the operation constraint.

solution.py14 lines

1# Full working Python code
2
3def maxFrequency(nums, k):
4    nums.sort()
5    left = 0
6    total = 0
7    max_freq = 0
8    for right in range(len(nums)):
9        total += nums[right]
10        while nums[right] * (right - left + 1) - total > k:
11            total -= nums[left]
12            left += 1
13        max_freq = max(max_freq, right - left + 1)
14    return max_freq

ℹ

Complexity note: The sorting step takes O(n log n) time, and the sliding window traversal is O(n), making this approach efficient overall.

1Sorting helps group similar elements together, making it easier to calculate operations.
2Using a sliding window allows us to efficiently find the longest subarray that meets the operation constraint.

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