No details shared about the specific problem, just that it was greedy-based.
Start by clarifying the problem and identifying the greedy choice property and optimal substructure. Then propose a greedy strategy, justify its correctness with an exchange argument, and analyze time/space complexity. Finally, discuss edge cases and potential pitfalls.
Pro tip: Always articulate why a greedy approach works here—interviewers at IBM value rigorous correctness proofs, not just code. If unsure, mention that you'd verify with a counterexample or consider dynamic programming as a fallback.
Ask questions to confirm input/output, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Explain why a local optimal choice leads to a global optimum. Discuss the greedy choice property and optimal substructure.
Outline the steps of the algorithm, including sorting or data structures if needed. Walk through a small example to illustrate.
Use an exchange argument or induction to show the greedy algorithm yields an optimal solution. Address why other approaches might fail.
State time and space complexity. Discuss edge cases (e.g., empty input, ties) and how the algorithm handles them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem requirements and constraints, then explain the heap-based solution, including the choice of min-heap or max-heap and the algorithm's steps. Walk through a small example to illustrate, and analyze time and space complexity.
Pro tip: Demonstrate deep understanding by discussing trade-offs between heap and other data structures, and mention real-world applications like priority queues or streaming median.
Ask questions to confirm input format, output requirements, constraints, and edge cases. Ensure you understand what the heap is used for.
Decide between min-heap and max-heap based on whether you need the smallest or largest elements efficiently. Justify your choice.
Describe step-by-step how to use the heap to solve the problem, including initialization, insertion, extraction, and any additional data structures.
Pick a small input and trace the algorithm, showing heap operations and intermediate states to verify correctness.
State time and space complexity, and discuss how the solution handles edge cases like empty input or duplicate values.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.