I started with a naive BFS approach and the interviewer let me run with it for a bit before asking what happens in a diamond dependency case.
Model the problem as a constraint satisfaction problem (CSP) where each package is a variable with its available versions as the domain, and dependency constraints are binary constraints between packages. Use backtracking search with constraint propagation (e.g., arc consistency) to find a valid assignment, and if no solution exists, explain why (e.g., conflicting constraints).
Pro tip: Mention that real-world resolvers like npm's use SAT solvers or PubGrub for efficiency, and discuss trade-offs between completeness and performance. Also, highlight the importance of handling version ranges and transitive dependencies correctly.
Ask about the scale (number of packages, versions), whether version constraints are exact or ranges (e.g., semver), and if there are any preferences (e.g., latest versions).
Define variables (packages), domains (available versions), and constraints (dependency requirements). Explain how transitive dependencies are captured.
Propose backtracking search with forward checking or arc consistency (AC-3) for pruning. Discuss alternative approaches like SAT solving or PubGrub and their trade-offs.
If no solution, explain how to detect conflicts (e.g., empty domain) and possibly suggest minimal unsatisfiable core. If multiple solutions, discuss heuristics for choosing one (e.g., latest versions).
Discuss worst-case exponential time, but note that real-world instances are often tractable with good heuristics. Mention caching, incremental solving, and parallelization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.