Give Min Sum
Reported by candidates from Deutsche Bank's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
Deutsche Bank hit you with Give Min Sum in September, and you've got 24-48 hours to lock it down. This is a greedy optimization problem where you need to find the minimum sum by making strategic choices about which elements to combine or reduce. The trick isn't brute force. It's recognizing that pairing the smallest values first yields the global minimum. StealthCoder will have the pattern the moment you see the problem statement, so you can focus on the implementation without blanking.
Pattern and pitfall
Give Min Sum is a classic greedy problem. The pattern is almost always: sort, then pair smallest elements first (often using a min-heap or priority queue). Each operation reduces the problem size and cumulative cost. The pitfall candidates hit is trying to be clever with dynamic programming or recursion when a single pass with the right greedy choice wins. The algorithm is stable and fast. On the live OA, if you blank on the approach, StealthCoder reads the constraints and feeds you the heap-based solution in seconds, letting you code with confidence instead of guessing.
Memorize the pattern. If you can't, run StealthCoder. The proctor sees the IDE. They don't see what's behind it.
You can drill Give Min Sum cold, or you can hedge it. StealthCoder runs invisibly during screen share and surfaces a working solution in under 2 seconds. The proctor sees the IDE. They don't see what's behind it. Made by an engineer who treats the OA as theater. If yours is tonight, you don't have time to grind. You have time to hedge.
Get StealthCoderRelated leaked OAs
This OA pattern shows up on LeetCode as minimum cost to connect sticks. If you have time before the OA, drill that.
You've seen the question.
Make sure you actually pass Deutsche Bank's OA.
Deutsche Bank reuses patterns across OAs. Made by an engineer who treats the OA as theater. If yours is tonight, you don't have time to grind. You have time to hedge. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Give Min Sum FAQ
Is this really just a min-heap problem?+
Almost always, yes. Sort or build a min-heap of the input, then repeatedly extract the two smallest elements, combine them, and push the result back. The total cost is the sum of all intermediate results. This greedy approach is provably optimal for most variants of this problem.
What if the problem asks for the minimum cost of merging?+
Same approach. Each merge has a cost (usually the sum of the two merged elements). Track the total cost across all merges. Min-heap ensures you always merge the cheapest pair next, minimizing total cost.
How do I handle ties or equal elements?+
Heap order handles this automatically. If two elements are equal, the heap treats them as interchangeable. Pick either one. The final sum won't change because you're always combining the smallest available values.
Can I solve this without a heap?+
You can sort once and simulate manually with two pointers, but a heap is cleaner and more intuitive. Many candidates sort and then iterate; both work if implemented correctly. Heap is lower risk in a timed setting.
What's the typical time and space complexity?+
O(n log n) for heap operations and sorting. Space is O(n) for the heap and output. This is efficient enough for most OA constraints. No surprises, no optimization tricks needed.