Is Largest Element in an Array after Merge Operations easy or hard?

The problem is rated Medium because the merge rule appears to require simulation, but the optimal solution relies on a greedy observation. Recognizing that right-to-left accumulation captures all valid merges is the key insight.

Largest Element in an Array after Merge Operations Python/Java solution

Most implementations use the greedy right-to-left scan. In Python or Java, maintain a running sum initialized with the last element and iterate backward through the array. Merge when the current value is less than or equal to the running sum, otherwise reset it.

How to solve Largest Element in an Array after Merge Operations in O(n)?

Traverse the array from right to left and keep a running value representing the current merged segment. If nums[i] is less than or equal to the running value, add it to the running sum. Otherwise, reset the running value to nums[i]. Track the maximum value seen during the traversal.

What is the best approach for Largest Element in an Array after Merge Operations?

The optimal approach is a greedy right-to-left traversal that cumulatively merges values. If the current element is less than or equal to the accumulated value, you merge them by adding it. Otherwise, you reset the accumulator. This runs in O(n) time and O(1) space and avoids explicitly simulating merges.

Is Largest Element in an Array after Merge Operations asked at Google/Amazon/Meta?

This problem reflects patterns commonly seen in Google and Amazon interviews, especially greedy array processing and merge simulation questions. Variants involving cumulative merging or monotonic processing appear in technical screens and online assessments.

What data structure is used in Largest Element in an Array after Merge Operations?

The optimal solution uses only a variable to track the cumulative merged value, making it a greedy array traversal. An alternative implementation uses a stack to simulate the merge process explicitly, which helps visualize how adjacent elements combine.

What is the time complexity of Largest Element in an Array after Merge Operations?

The optimal greedy solution runs in O(n) time because the array is scanned once from right to left. Each element participates in at most one merge decision. Space complexity is O(1) since only a running sum and maximum value are stored.

Description Hints 2 Solution Video 9:59

2789. Largest Element in an Array after Merge Operations

Medium48.3% acceptance2 companies

You are given a 0-indexed array nums consisting of positive integers.

You can do the following operation on the array any number of times:

Choose an integer i such that 0 <= i < nums.length - 1 and nums[i] <= nums[i + 1]. Replace the element nums[i + 1] with nums[i] + nums[i + 1] and delete the element nums[i] from the array.

Return the value of the largest element that you can possibly obtain in the final array.

Example 1:

Input: nums = [2,3,7,9,3]
Output: 21
Explanation: We can apply the following operations on the array:
- Choose i = 0. The resulting array will be nums = [5,7,9,3].
- Choose i = 1. The resulting array will be nums = [5,16,3].
- Choose i = 0. The resulting array will be nums = [21,3].
The largest element in the final array is 21. It can be shown that we cannot obtain a larger element.

Example 2:

Input: nums = [5,3,3]
Output: 11
Explanation: We can do the following operations on the array:
- Choose i = 1. The resulting array will be nums = [5,6].
- Choose i = 0. The resulting array will be nums = [11].
There is only one element in the final array, which is 11.

Constraints: