Find Min Cost
Reported by candidates from JP Morgan's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
JP Morgan's April 2024 OA threw candidates at a cost-minimization problem where greedy intuition breaks down fast. You're probably thinking of a straightforward pick-the-cheapest-option approach, but the real trick is understanding what's actually being minimized and when a local choice kills the global answer. If you blank on the pattern during the live assessment, StealthCoder will catch the greedy structure and walk you through the state space without the proctor seeing a thing.
Pattern and pitfall
The problem tests whether you'll blindly pick minimum costs at each step or step back to see the optimization landscape. Greedy works here, but only if you're greedy about the right thing. Common trap: sorting by cost alone and missing dependencies, constraints, or the true cost function. The pattern usually involves selecting items, resources, or operations such that each local choice contributes to the global minimum. You'll likely need to sort by a derived metric (cost per unit, cost per depth, ratio of factors) rather than raw cost. Walk through small examples by hand first. Identify what changes with each decision. StealthCoder as a live safety net lets you pattern-match if you get stuck on formulation.
If this hits your live OA and you blank, StealthCoder solves it in seconds, invisible to the proctor.
You can drill Find 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 would have shipped this the night before his JPMorgan OA if he'd had it.
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JP Morgan reuses patterns across OAs. Built by an Amazon engineer who would have shipped this the night before his JPMorgan OA if he'd had it. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Find Min Cost FAQ
Is this really greedy or is it dynamic programming?+
The hint says greedy, so start there. DP feels tempting because cost problems often hint at optimal substructure, but if the local choice provably leads to the global optimum without backtracking, greedy wins. Test your greedy with a counterexample on paper first.
What's the most common mistake candidates make?+
Sorting by cost directly and not asking what the cost actually measures. Is it cost per item, cost per operation, cost to combine, cost to remove? Read the problem twice. The metric you sort by is the whole game.
How do I know if my greedy choice is safe?+
After you pick, ask: could a different choice have led to a better overall answer. If the answer is always no, you're greedy. If it's maybe, you need DP or backtracking. Sketch a proof on the example.
Will this problem involve sorting?+
Very likely. Greedy cost problems almost always sort by some metric first. Figure out what that metric is from the problem statement and examples. That's 80% of the solution.
How much time should I spend on this in the OA?+
15 minutes to understand what you're minimizing, 10 minutes to sketch the greedy approach with an example, 20 minutes to code. If you're stuck after 30 minutes, flag it and come back. Don't optimize until the logic is solid.