I went in expecting a standard algorithm question and got something completely different.
Start by clarifying requirements and constraints, then design a class hierarchy that encapsulates state and operations. Implement the greedy component with a clear invariant, and be prepared to refactor as new constraints are introduced, explaining trade-offs at each step.
Pro tip: Verbalize your thought process and explicitly state assumptions; interviewers at Citadel value structured reasoning and adaptability over perfect code. When constraints change, pause to reassess the design before coding, showing you can pivot without panic.
Ask questions to understand the data structure's expected operations, performance requirements, and any initial constraints. Confirm the greedy optimization goal and how new constraints might be introduced.
Outline classes, interfaces, and key methods, focusing on encapsulation and separation of concerns. Explain how state will be maintained and updated.
Describe the greedy algorithm, including the selection criterion and proof of correctness or invariant. Discuss how it integrates with the data structure.
When new constraints are introduced, analyze their impact on the current design. Propose modifications, such as changing data structures or algorithm, and explain trade-offs.
Walk through test cases, including edge cases and performance under new constraints. Verify correctness and efficiency, and suggest potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.