My first instinct was to just sort both strings and compare, which works fine for the anagram check.
Clarify the problem constraints and edge cases first, then propose an efficient algorithm using character frequency counting (e.g., a hash map or fixed-size array) to check anagrams. After identifying all anagram candidates, apply the tie-breaking rule by checking the first character, and return the appropriate result or empty string.
Pro tip: Mention that you can optimize by pre-filtering candidates based on length and first character before doing full anagram checks, which can significantly reduce unnecessary comparisons in large datasets.
Ask about input constraints (e.g., string lengths, character set, case sensitivity) and edge cases like empty strings, multiple matches, or no matches. Confirm the tie-breaking rule: if multiple anagrams exist, return the one whose first character matches the target's first character.
Decide between sorting (O(n log n)) or character frequency counting (O(n)) for comparing strings. Frequency counting with a hash map or array is more efficient for large strings, especially if the character set is known.
Loop through the candidate list, check if each candidate is an anagram of the target using your chosen method. Collect all anagrams that match, and also track those whose first character matches the target's first character.
If exactly one anagram is found, return it. If multiple, return the first one (in the order they appear) that has the same first character as the target. If none qualify, return an empty string.
State the time and space complexity of your solution (e.g., O(N * L) where N is number of candidates and L is average length). Walk through test cases including no match, single match, multiple matches with and without first-character tie-break.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.