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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Google coding interview with one algorithmic problem. Short and to the point, nothing fancy.

Questions Asked (1)

Q1

Explain and solve the 3-partition problem.

Algorithms & Data Structures
Author's notes

Took me a second to even remember what the 3-partition problem was.

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AI HintsAI Generated

Suggested Approach

Start by clearly defining the 3-partition problem and its NP-complete nature, then outline a solution approach such as dynamic programming for small sums or backtracking with pruning for general cases. Emphasize the importance of considering constraints and discussing trade-offs between exact and heuristic methods.

Pro tip: Mention that while 3-partition is NP-complete, practical instances often have small sums or can be solved with pseudo-polynomial DP; also note that the problem is strongly NP-complete, so no FPTAS exists unless P=NP.

1. Define the problem

State that given a multiset of 3m positive integers, the goal is to partition it into m triples each summing to the same target T (total sum / m). Clarify that it's a decision problem.

2. Discuss complexity

Explain that 3-partition is NP-complete in the strong sense, meaning it remains NP-complete even when numbers are bounded by a polynomial in m. This rules out pseudo-polynomial algorithms unless P=NP.

3. Present a DP solution for small sums

If the target sum T is small, use dynamic programming with state (index, sum1, sum2, sum3) or a 2D DP over sums to track achievable subset sums. Complexity O(n * T^2) or O(n * T^3) depending on implementation.

4. Describe backtracking with pruning

For general cases, use backtracking: sort numbers descending, try to place each number into one of m bins, prune when a bin exceeds T or when remaining numbers cannot fill bins. Use symmetry breaking to avoid duplicate states.

5. Mention heuristics and approximation

Since exact solution is hard, discuss greedy heuristics (e.g., first-fit decreasing) or approximation algorithms, noting that no constant-factor approximation exists for 3-partition unless P=NP.

Key Points to Mention

  • 3-partition is NP-complete in the strong sense, unlike partition which is weakly NP-complete.
  • Dynamic programming works when the target sum T is polynomial in input size, with state space O(T^2) or O(T^3).
  • Backtracking with pruning and symmetry breaking is practical for moderate-sized instances.
  • No fully polynomial-time approximation scheme (FPTAS) exists unless P=NP.
  • The problem is different from 3-dimensional matching and can be reduced from it.
  • Edge cases: total sum not divisible by m, numbers larger than T, or m=1.

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