Maximum Frequency of an Element After Performing Operations II - Solution & Explanation

This approach leverages a sorted array and the sliding window approach to maximize the frequency of elements. The idea is to use the sliding window to keep track of a subarray where you can potentially make all elements equal by performing add operations within the given range [-k, k].

You maintain a window that only expands when the needed operations are within the limit specified by numOperations. This involves checking the operations needed to turn the smallest element in the window into the largest element and adjusting it as necessary to stretch the window.

The C code uses quick-sort to sort the array and then applies a sliding window to calculate the maximum frequency of any number that can be achieved by performing operations. The steps:

• Sort the array.
• Use a loop with two pointers left and right to maintain the window.
• Calculate operations needed to make all numbers in this window equal to the number at the right pointer index and adjust the left pointer accordingly.
• Continuously update the result with the maximum window size encountered.

This approach focuses on efficiently maximizing the frequency using a greedy strategy with a series of modifications limited by numOperations. It involves selectively manipulating elements to bunch them toward an optimal frequency level after sorting the array.

The process aims to utilize available changes strategically for maximizing occurrences of a particular value obtained.

The C++ code example applies sorting followed by greedy decision making:

• First, sort the array.
• Utilize a slipping window by maximizing frequencies with allowable operation limitations calculated during traversal.
• The sliding window shrinks when current needed operations for uniformity exceed specified k.
• Use comparisons to update maximum frequency efficiently.