Start by clarifying the problem constraints (e.g., array size, value range, duplicates) and then propose an efficient solution using a min-heap of size k+1 to find the k-th largest element in O(n log k) time. Alternatively, discuss the Quickselect algorithm for average O(n) time, but note its worst-case O(n^2) and potential for recursion depth issues. Finally, analyze trade-offs and mention edge cases.
Pro tip: Meta interviewers value clean, bug-free code and strong communication. Practice explaining your thought process while coding, and always test with edge cases like k=0, k=n-1, and arrays with duplicates.
Ask about input size, value range, whether duplicates count as separate elements, and if the array can be modified. This shows attention to detail and helps choose the right approach.
Mention sorting (O(n log n)), min-heap of size k+1 (O(n log k)), and Quickselect (average O(n)). Compare their trade-offs in terms of time, space, and worst-case performance.
For most interviews, the heap approach is a safe bet: iterate through the array, maintain a min-heap of size k+1, and after processing, the root is the k-th largest. Write clean code with meaningful variable names.
Walk through examples like k=0 (max), k=n-1 (min), arrays with duplicates, and single-element arrays. Verify that the heap size logic correctly handles these cases.
State time and space complexity clearly. If time permits, mention that Quickselect can be more efficient on average but requires careful handling of worst-case scenarios.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.