Equal Sum Arrays With Minimum Number of Operations
A medium-tier problem at 54% community acceptance, tagged with Array, Hash Table, Greedy. Reported in interviews at American Express and 0 others.
You've got two arrays and you need to make their sums equal. The catch: you can only increment one array's elements or decrement the other's, one operation at a time. American Express has asked this. The acceptance rate sits at 54%, which means half the candidates either miss the greedy insight or get tangled in implementation. The trick isn't brute force. It's about which elements to target first. If this problem hits your live assessment and you blank on the priority order, StealthCoder surfaces the working solution in seconds, invisible to the proctor.
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Equal Sum Arrays With Minimum Number of Operations is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage.
Get StealthCoderThe problem demands a greedy approach: always operate on the largest gap between the two arrays. Start by computing the difference between their sums. If array A's sum is smaller, you want to either increment A's smallest elements or decrement B's largest elements. The key insight is treating both operations as equivalent (reducing the gap) and always attacking the element that gives you the biggest immediate win. Hash tables or sorted structures help track which elements to pick next. Candidates often try to simulate the operations one by one, which tanks performance. Others miscalculate the target sum or apply greedy choices incorrectly. If you've drilled array problems and hash table basics but haven't seen this specific greedy pattern, StealthCoder is your safety net on test day.
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Equal Sum Arrays With Minimum Number of Operations recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Equal Sum Arrays With Minimum Number of Operations interview FAQ
What's the actual trick to this problem?+
It's pure greedy. Calculate the sum difference. Then always pick the element that closes that gap the most in one operation. Don't try to balance incrementally. Sort or hash the candidates, pick the biggest delta each time, and count operations until the sums match. The pattern matters more than raw implementation speed.
Why is the acceptance rate only 54%?+
Most failures come from either missing the greedy principle entirely (trying brute force or incorrect balancing logic) or implementing the greedy choice wrong. Candidates also overthink it as a math problem when it's really about sorting and picking the max delta repeatedly.
Is this still asked at American Express?+
American Express has reported this problem. It's not ultra-frequent across the board, but it does surface in their assessments. If you're targeting them specifically, this is worth a solid review. For other companies, medium-difficulty greedy array problems like this are common.
How does this relate to hash tables and counting?+
Hash tables let you track element frequencies and avoid duplicates when needed. Counting sorts or frequency maps can speed up the greedy selection. You don't strictly need them, but they make the solution cleaner and handle edge cases like duplicate values better.
What's the most common mistake?+
Incrementing or decrementing randomly instead of targeting the largest gap first. Candidates often try to alternate between arrays or apply rules that sound logical but don't minimize operation count. Greedy only works if you always pick the element that moves the needle most.
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