Get Min Cost
Reported by candidates from DE Shaw's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
DE Shaw's Get Min Cost hit the assessment circuit in May 2024, and it's a greedy problem disguised as an optimization puzzle. You're looking at a cost-minimization scenario where the order of operations matters more than you'd think. Most candidates overthink it and miss the greedy insight on the first pass. StealthCoder catches that moment when you're stuck between two approaches and need the pattern lock confirmed in real time.
Pattern and pitfall
The greedy trap here is assuming you should always pick the locally optimal choice, but DE Shaw's twist forces you to think about which operations compound. The real pattern: identify the operation that reduces future costs the most, then repeat. It's not about picking the smallest individual cost each time. It's about the sequence that bottoms out the total. Common pitfall is simulating greedily without verifying your choice actually minimizes the global sum. The algorithm is usually O(n log n) or O(n) depending on the data structure you use to track available operations. If you blank during the OA, StealthCoder will confirm whether you're building a priority queue, sorting by some hidden metric, or using a simulation with early termination.
If you see this problem in your OA tomorrow, the play is to recognize the pattern in 30 seconds. StealthCoder buys you that recognition.
You can drill Get Min Cost 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. Built by an Amazon engineer who passed his OA cold and still thinks the filter is broken.
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DE Shaw reuses patterns across OAs. Built by an Amazon engineer who passed his OA cold and still thinks the filter is broken. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Get Min Cost FAQ
Is this problem really greedy or is it dynamic programming?+
It's greedy. DP would be overkill and slower. The trick is finding the right greedy criterion (what makes an operation 'best'). Once you identify that metric, the solution flows. DP often means you missed the greedy insight.
What's the most common wrong answer on this one?+
Picking the smallest individual operation cost each time without considering cascading effects. Or greedily minimizing without tracking which operations are still available. You need to ensure your greedy choice doesn't lock you into a worse state later.
How much time should I spend on this in the OA?+
If you recognize the greedy pattern, 15-20 minutes max for code. If you're hunting for the pattern, you'll burn 30-40 minutes. If you hit 25 minutes without a coherent approach, pivot and skip. Partial credit is real.
Do I need to precompute anything before the greedy loop?+
Probably. Most candidates sort or build a data structure first. That's often the 'setup' phase. The greedy loop then iterates through available options and picks the best by your criterion, removing it from the pool.
What if there are multiple valid orderings of operations?+
Then your greedy criterion must break ties consistently. DE Shaw will test edge cases where two operations look equally good. Make sure your comparison function is total and deterministic.