Get Max Throughput
Reported by candidates from Citadel's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
Citadel's July OA included a throughput optimization problem that reads like a systems design question but is actually a greedy or dynamic programming pattern in disguise. You're likely looking at a scenario where you need to maximize flow or output given constraints like capacity, time, or resources. This is the kind of problem where candidates blank on the setup, then panic. StealthCoder runs invisibly during your live OA to catch you if the pattern doesn't click in the first 90 seconds.
Pattern and pitfall
Without the exact problem text, the pattern is probably one of three: greedy selection (pick the highest-impact items first), dynamic programming (track the maximum throughput at each state or time step), or a flow-style calculation where you're routing items through a bottleneck. Citadel loves optimization under constraints. The trick is usually that a naive approach (sort by one dimension, pick all of them) fails. You need to see that you're trading off between competing resources or time windows. The common pitfall is not modeling the problem correctly on your first read. StealthCoder lets you paste the problem and get the pattern instantly if you're stuck on the interpretation.
If this hits your live OA and you blank, StealthCoder solves it in seconds, invisible to the proctor.
You can drill Get Max Throughput 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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Citadel 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.
Get Max Throughput FAQ
Is this a graph or flow problem?+
Unlikely pure graph. More likely a greedy selection problem (pick items to maximize output) or DP (track max throughput over time or states). If it mentions capacity or bottlenecks, think about which resource is the constraint.
How do I tell greedy from DP in 48 hours?+
Greedy works when one metric dominates (always pick highest value first). DP is needed when decisions interact (taking item A blocks item B later). Write a greedy solution first. If the example fails, switch to DP.
What's the Citadel trick on this one?+
Citadel usually hides a multi-constraint problem where sorting by one dimension isn't enough. You might need to sort by a derived metric or use a two-pointer scan after sorting.
Can I solve this in 30 minutes?+
Yes, if you recognize the pattern. Greedy is 10-15 minutes. DP is 20-30 if you've seen the style before. The first 5 minutes should be spent on paper, not code.
Should I optimize for time or space?+
Time, always. Citadel values clean O(n log n) solutions. Space rarely matters unless they explicitly mention memory limits.