Start by clarifying the requirements and constraints, then propose a sparse representation such as a dictionary or coordinate list that stores only non-zero elements. Discuss the trade-offs between different sparse structures and mention how operations like access, insertion, and iteration would work.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that the best choice depends on the sparsity pattern and access patterns—for example, if you need fast random access, a hash map might be better, but if you need efficient iteration, a sorted array of indices could be preferable.
Ask about the expected operations (read, write, iterate), the degree of sparsity, and whether the vector size is fixed or dynamic. This shows you think before coding.
Suggest storing only non-zero elements, e.g., using a dictionary mapping indices to values, or a list of (index, value) pairs. Explain why this saves memory.
Compare options like hash maps (fast access, more memory overhead) vs. sorted arrays (memory efficient, slower updates) vs. compressed sparse row (CSR) for matrix-like data. Mention time complexities for key operations.
Explain how to implement get, set, and iterate efficiently. For example, with a hash map, get/set are O(1) average; with a sorted array, get is O(log n) via binary search.
Mention handling of zero values (removing entries when set to zero), memory overhead of the structure itself, and potential for further compression if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.