Bytedance·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Bytedance SWE interview with a coding question that extended the classic integer square root problem into floating point territory. Pretty straightforward if you've seen binary search on real intervals before, but the precision angle adds enough complexity that it's worth thinking through carefully.

Questions Asked (1)

Given a non-negative integer and a precision value, return the square root of the integer rounded (or truncated) to that many decimal places. Be ready to discuss multiple approaches and their time complexities.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went with binary search on a real interval and it worked, but I fumbled the complexity analysis for a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: confirm whether rounding or truncation is required, and discuss edge cases like zero and perfect squares. Then present multiple approaches: binary search on the answer, Newton's method, and digit-by-digit, comparing their time complexities and trade-offs. Finally, implement the most efficient approach with careful handling of precision and integer overflow.

Pro tip: Mention that binary search can be done on integers scaled by 10^precision to avoid floating-point errors, and that Newton's method converges quadratically but may need a termination condition based on precision.

1. Clarify requirements and edge cases

Ask whether the result should be rounded or truncated, and confirm the input range. Discuss edge cases such as n=0, n=1, and perfect squares.

2. Propose multiple approaches

Outline binary search, Newton's method, and digit-by-digit methods. Briefly explain how each works and their time complexities.

3. Compare trade-offs

Compare the approaches in terms of time complexity, space complexity, implementation difficulty, and numerical stability. Recommend the best one for the given constraints.

4. Implement chosen approach

Write clean code for the selected method, handling precision and overflow. For binary search, search over integers scaled by 10^precision; for Newton's method, iterate until the desired precision is reached.

5. Test and verify

Test with various inputs including edge cases and verify the result matches the expected precision. Discuss potential pitfalls like floating-point errors.

Key Points to Mention

Binary search on the answer space with integer scaling to avoid floating-point precision issues.
Newton's method (Newton-Raphson) for square root, its quadratic convergence, and termination criteria.
Time complexity analysis: binary search O(log(n * 10^precision)), Newton's method O(log(precision)) iterations with each iteration O(1) for basic arithmetic.
Handling of edge cases: n=0, n=1, perfect squares, and large integers.
Difference between rounding and truncation, and how to implement each.
Potential integer overflow when scaling by 10^precision, and using appropriate data types (e.g., long long).

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