My first move was sorting each string and using that as a hash map key, which works fine.
Clarify assumptions and edge cases, then propose a hash map approach where the key is a canonical representation of each string's character frequencies (e.g., sorted string or count tuple). Walk through the algorithm, analyze time/space complexity, and discuss potential optimizations or trade-offs.
Pro tip: Mention that sorting each string to form the key takes O(k log k) per string, but using a character count array as the key can achieve O(k) per string, which is more efficient for large alphabets or long strings. Also, note that the order of groups doesn't matter, so you can return the hash map values directly.
Confirm input constraints (e.g., string length, character set, empty strings) and output format. Define what makes two strings anagrams.
Decide on a canonical representation for anagrams: sorted string or frequency count array/tuple. Discuss trade-offs in time and space.
Iterate through the array, compute the key for each string, and append the string to the corresponding list in a hash map.
Return the values of the hash map as the grouped anagrams. Order doesn't matter.
State time complexity: O(n * k) if using count array, O(n * k log k) if sorting. Space complexity: O(n * k) for storing all strings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.