Is Maximum Frequency After Subarray Operation easy or hard?

Maximum Frequency After Subarray Operation is generally classified as a Medium problem. The challenge lies in recognizing that the operation can be modeled as a maximum subarray gain problem rather than brute‑forcing all subarrays.

Maximum Frequency After Subarray Operation Python/Java solution

The standard implementation iterates through the array, computes the gain for each element relative to a target value, and applies Kadane’s algorithm to find the maximum gain segment. The same logic translates directly to Python, Java, C++, and Go with O(n) time complexity.

How to solve Maximum Frequency After Subarray Operation in O(n)?

Track the base count of the target value in the array. Convert the effect of applying the operation into a gain array where beneficial conversions add +1 and harmful changes add -1. The best subarray to operate on is the maximum subarray sum, which can be computed in linear time using Kadane’s algorithm.

What is the best approach for Maximum Frequency After Subarray Operation?

The most efficient approach models the operation as a gain/loss problem and uses Kadane’s algorithm to find the best subarray to modify. First count the base frequency of a target value. Then compute how many additional elements can be converted by applying a single shift to a subarray. This results in an O(n) time solution.

Is Maximum Frequency After Subarray Operation asked at Google/Amazon/Meta?

Problems involving maximum frequency after modifications and subarray gain optimization are common in interviews at companies like Google, Amazon, and Meta. They typically test understanding of greedy techniques, prefix reasoning, and Kadane-style dynamic programming.

What data structure is used in Maximum Frequency After Subarray Operation?

The solution commonly uses arrays for iteration and a hash table to count frequencies of values. Dynamic programming logic similar to Kadane’s algorithm is applied to evaluate the best subarray transformation.

What is the time complexity of Maximum Frequency After Subarray Operation?

The optimal solution runs in O(n) time using a greedy maximum-subarray strategy similar to Kadane’s algorithm. Earlier brute-force approaches that check all subarrays require O(n^2) or O(n^3) time, which is too slow for large inputs.

Description Hints 2 Video 20:06

3434. Maximum Frequency After Subarray Operation

Medium31.6% acceptance4 companies

You are given an array nums of length n. You are also given an integer k.

You perform the following operation on nums once:

Select a subarray nums[i..j] where 0 <= i <= j <= n - 1.
Select an integer x and add x to all the elements in nums[i..j].

Find the maximum frequency of the value k after the operation.

Example 1:

Input: nums = [1,2,3,4,5,6], k = 1

Output: 2

Explanation:

After adding -5 to nums[2..5], 1 has a frequency of 2 in [1, 2, -2, -1, 0, 1].

Example 2:

Input: nums = [10,2,3,4,5,5,4,3,2,2], k = 10

Output: 4

Explanation:

After adding 8 to nums[1..9], 10 has a frequency of 4 in [10, 10, 11, 12, 13, 13, 12, 11, 10, 10].

Constraints:

1 <= n == nums.length <= 10⁵
1 <= nums[i] <= 50
1 <= k <= 50

Asked at 4 companies

Amazon

Google Microsoft Bloomberg

Topics

Hints open one at a time

Hint 1

Fix the element you want to convert to k.

Hint 2

Use prefix sums to optimize counting occurrences of an element.

LanguageLoading editor…

1class Solution:
2    def maxFrequency(self, nums: List[int], k: int) -> int:
3        

Starter codePython 3Spaces: 4

Examples

Case 1Case 2

Input

[1,2,3,4,5,6]
1

Input

[10,2,3,4,5,5,4,3,2,2]
10

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