Common Patterns
Greedy & Bin Packing
When local optimization leads to global solutions — greedy strategies, bin packing heuristics, and the tradeoffs interviewers expect you to reason about.
Problems involving resource allocation, scheduling, interval management, and bin packing are natural fits for multi-file coding challenges because they're easy to state but surprisingly tricky to get right. This almost always leads us to greedy algorithms as a candidate solution. The part that makes them interesting interview material is the tradeoff between correctness and speed: a greedy approach runs fast, but does it actually give you the right answer?
Interviewers are not testing whether you've memorized the a problem from your algorithms class. They want to see you reason about why a greedy approach works for a given problem, or catch when it doesn't. The AI will happily generate a greedy solution for almost anything you throw at it. Your job is to know when that's correct and when it's dangerously wrong.
You don't need deep prior expertise with greedy algorithms going into these interviews. Interviewers expect you to assimilate the core ideas quickly and apply them to unfamiliar problems. What matters is your reasoning process, not whether you've seen the exact problem before.
Recognizing the pattern
Greedy is the trickiest pattern to recognize because the opportunity is everywhere and the correctness is conditional. Almost any optimization problem looks like it might submit to a greedy solution — make the locally best choice and hope it works out. The skill is knowing when "hope" is enough and when you need to actually verify.
Tells that greedy is worth considering:
- An optimization goal with a natural ordering. Maximum non-overlapping intervals (order by end time), minimum bins (order by size), shortest job first. If the items have an obvious sort key, greedy is in the candidate set.
- The choice at step k doesn't constrain step k+1. Or at least, doesn't constrain it much. If picking the "best" thing now doesn't paint you into a corner later, greedy probably works.
- Resource allocation, scheduling, interval management, packing. These domains have a long history of clean greedy solutions, and interviewers love them for the same reason.
But greedy is also the pattern most likely to be plausibly wrong. The AI will happily produce a greedy solution to a problem where greedy gives the wrong answer — and the code will compile, run, and pass the test cases the interviewer gave you. So when you recognize a possibly-greedy problem, the immediate next move is to verify the greedy choice property holds, not to ship the code.
Ask three diagnostic questions before committing:
- Can I make a choice and never need to undo it? If not, greedy won't work.
- After making one choice, does the remaining problem look like a smaller version of the same thing? If not, optimal substructure doesn't hold.
- Can I construct a small counterexample where greedy gives the wrong answer? If yes, you've just proved greedy is wrong. If you can't find one after a couple of minutes, greedy is probably fine, but that's not a proof.
The greedy paradigm
An example: coin change
Interval scheduling
Bin packing
The main strategies
Real-world framing
Multi-dimensional bin packing
When to use vs. alternatives
Prompting the AI
Verifying the AI's code
What interviewers expect
Putting it together
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