The key move is encoding each string as a bitmask over the alphabet, then checking if the candidate's bits are all present in the target's bits using bitwise AND against the complement.
Clarify the problem and constraints, then propose an efficient solution using bitmasks to represent character sets. Explain the algorithm step-by-step, analyze time and space complexity, and discuss trade-offs between different approaches.
Pro tip: Demonstrate awareness of Unicode and character encoding by asking whether the input is limited to ASCII or may include Unicode, and mention that bitmasking is efficient for small alphabets but a hash set is more general.
Ask about input size, character set (ASCII vs Unicode), case sensitivity, and whether the output order matters. Confirm that 'distinct characters' means unique characters ignoring duplicates.
Represent each string's distinct characters as a bitmask (e.g., 26 bits for lowercase English letters). Precompute the target's bitmask, then for each candidate, check if (candidate_mask & ~target_mask) == 0.
Time: O(T + sum of candidate lengths) with bitmask operations; space: O(1) per string. Discuss alternative hash set approach: O(T + sum of candidate lengths) time but higher constant factors and memory.
Consider empty strings, empty candidate list, and large alphabets (e.g., Unicode). For large alphabets, suggest using a hash set or a boolean array of size equal to the alphabet.
Implement the solution with clear variable names and comments. Optimize by early termination if a candidate contains a character not in the target.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.