Is Equal Sum Arrays With Minimum Number of Operations easy or hard?

The problem is classified as Medium because the brute idea is simple but the optimal solution requires recognizing the bounded value range and applying a greedy counting strategy to achieve O(n) time.

Equal Sum Arrays With Minimum Number of Operations Python/Java solution

Most implementations follow the greedy counting approach. Compute the sum difference, count potential improvements from each element, and greedily consume the largest reductions until the difference is eliminated. The same logic translates cleanly to Python, Java, C++, C#, and JavaScript.

How to solve Equal Sum Arrays With Minimum Number of Operations in O(n)?

First compute the sums of both arrays and determine the difference. For each element, calculate the maximum change it could contribute toward reducing that difference. Store counts of possible improvements (1 through 5). Greedily apply the largest improvements until the difference becomes zero or negative, which takes linear time.

What is the best approach for Equal Sum Arrays With Minimum Number of Operations?

The best approach is greedy difference reduction using counting. Calculate the sum difference between the arrays and track how much each element could change that difference (up to 5 because values range from 1 to 6). Process the largest possible reductions first using a frequency array. This runs in O(n) time and O(1) space.

Is Equal Sum Arrays With Minimum Number of Operations asked at Google/Amazon/Meta?

Greedy array optimization problems with bounded values appear frequently in interviews at companies like Amazon, Google, and Meta. This problem tests your ability to model operations as maximum improvements and apply greedy selection efficiently.

What data structure is used in Equal Sum Arrays With Minimum Number of Operations?

The optimal solution uses a counting array to track possible difference reductions. An alternative implementation uses a max heap (priority queue) to repeatedly select the operation with the largest impact.

What is the time complexity of Equal Sum Arrays With Minimum Number of Operations?

The optimal greedy counting solution runs in O(n) time where n is the total number of elements across both arrays. It uses O(1) extra space because the possible improvement values are limited to 1–5. A heap-based alternative runs in O(n log n) time due to priority queue operations.

Description Hints 2 Solution Video 15:29

1775. Equal Sum Arrays With Minimum Number of Operations

Medium54.6% acceptance1 company

You are given two arrays of integers nums1 and nums2, possibly of different lengths. The values in the arrays are between 1 and 6, inclusive.

In one operation, you can change any integer's value in any of the arrays to any value between 1 and 6, inclusive.

Return the minimum number of operations required to make the sum of values in nums1 equal to the sum of values in nums2. Return -1 if it is not possible to make the sum of the two arrays equal.

Example 1:

Input: nums1 = [1,2,3,4,5,6], nums2 = [1,1,2,2,2,2]
Output: 3
Explanation: You can make the sums of nums1 and nums2 equal with 3 operations. All indices are 0-indexed.
- Change nums2[0] to 6. nums1 = [1,2,3,4,5,6], nums2 = [6,1,2,2,2,2].
- Change nums1[5] to 1. nums1 = [1,2,3,4,5,1], nums2 = [6,1,2,2,2,2].
- Change nums1[2] to 2. nums1 = [1,2,2,4,5,1], nums2 = [6,1,2,2,2,2].

Example 2:

Input: nums1 = [1,1,1,1,1,1,1], nums2 = [6]
Output: -1
Explanation: There is no way to decrease the sum of nums1 or to increase the sum of nums2 to make them equal.

Example 3:

Input: nums1 = [6,6], nums2 = [1]
Output: 3
Explanation: You can make the sums of nums1 and nums2 equal with 3 operations. All indices are 0-indexed. 
- Change nums1[0] to 2. nums1 = [2,6], nums2 = [1].
- Change nums1[1] to 2. nums1 = [2,2], nums2 = [1].
- Change nums2[0] to 4. nums1 = [2,2], nums2 = [4].

Constraints:

1 <= nums1.length, nums2.length <= 10⁵
1 <= nums1[i], nums2[i] <= 6

Asked at 1 company

American Express

Topics

Hints open one at a time

Hint 1

Let's note that we want to either decrease the sum of the array with a larger sum or increase the array's sum with the smaller sum.

Hint 2

You can maintain the largest increase or decrease you can make in a binary search tree and each time get the maximum one.

15:29

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