Citadel·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Citadel SWE interview with a code optimization problem. You get a slow but correct C++ function and have to make it fast, which sounds straightforward until you're actually staring at it under pressure.

Questions Asked (1)

You're given a correct but slow C++ function. Identify the bottleneck through complexity analysis and refactor it for significantly better performance, preserving the original signature and behavior. The expected solution is only a few lines.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The key was recognizing the O(N^2) loop and knowing which structure to swap in.

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AI HintsAI Generated

Suggested Approach

First, analyze the given function's time and space complexity to pinpoint the bottleneck, such as nested loops or redundant computations. Then, refactor using a more efficient algorithm or data structure, ensuring the original signature and behavior are preserved. Finally, verify correctness and performance improvement with test cases.

Pro tip: At Citadel, interviewers value clear communication of trade-offs and rigorous testing; always discuss the complexity improvement and edge cases before writing code.

1. Understand the function

Read the code carefully to understand its purpose, inputs, outputs, and any side effects. Identify the expected behavior that must be preserved.

2. Analyze complexity

Determine the time and space complexity of the current implementation. Look for nested loops, repeated work, or inefficient data structures that cause the bottleneck.

3. Identify optimization

Based on the complexity analysis, select a more efficient algorithm or data structure (e.g., hash map, two-pointer, prefix sums) that reduces the dominant term.

4. Refactor code

Implement the optimized solution in a few lines, keeping the function signature and behavior identical. Ensure no unintended changes.

5. Verify and discuss

Test with edge cases and compare outputs to the original. Explain the new complexity and any trade-offs made.

Key Points to Mention

Time and space complexity analysis (Big O notation)
Identifying the bottleneck (e.g., O(n^2) loops, redundant computations)
Choosing appropriate data structures (e.g., hash map, set) for O(1) lookups
Preserving function signature and behavior
Testing edge cases and correctness
Discussing trade-offs (e.g., time vs. space, readability)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.