← Applied intuition Interview Insights
My first instinct was to just reach for a hashmap keyed on the array contents, which worked fine conceptually, but I fumbled around for a bit figuring out how to hash a small fixed-length array cleanly.
Start by clarifying the problem constraints (e.g., vertex format, size, memory limits) and then propose a hash-based solution using a dictionary to map vertex tuples to their first-occurrence indices. Iterate through the original list, building the deduplicated list and index mapping in one pass, and discuss trade-offs like time vs. space complexity.
Pro tip: Mention that you would use a hash of the vertex data (e.g., a tuple of floats) as the key, but be aware of floating-point precision issues and consider using a tolerance-based comparison if exact duplicates are not guaranteed.
Ask about vertex data type (e.g., floats, integers), expected input size, memory limits, and whether exact duplicates or near-duplicates need handling. This ensures the solution fits the context.
Use a hash map (dictionary) to store each unique vertex as a key and its index in the deduplicated list as the value. This provides O(1) average-time lookups.
Traverse the original list, and for each vertex, check if it exists in the hash map. If not, add it to the deduplicated list and record its index; then append the index to the mapping array.
State that the solution runs in O(n) time and O(n) space, where n is the number of vertices. Discuss alternatives like sorting-based deduplication (O(n log n)) and their trade-offs.
Consider empty input, all duplicates, and floating-point precision. Suggest using a custom hash or rounding if needed, and mention potential memory optimizations like storing indices as 32-bit integers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.