← Bloomberg Interview Insights
I knew this one but still fumbled the explanation a bit.
Use a sliding window with two pointers to maintain a substring with unique characters, expanding the right pointer and shrinking the left when a duplicate is found. Track the maximum length seen. This yields O(n) time and O(min(n, alphabet)) space.
Pro tip: Clarify assumptions upfront (e.g., character set, case sensitivity) and discuss trade-offs between the optimal sliding window and a brute-force approach to show depth.
Ask about the character set (ASCII, Unicode), case sensitivity, and expected input size to determine the appropriate approach and data structures.
Mention that a naive solution checks all substrings for uniqueness, which is O(n^3) or O(n^2) with optimization, but is inefficient for large inputs.
Explain the two-pointer technique: expand right to include new characters, and when a duplicate is found, move left past the previous occurrence to maintain a window of unique characters.
Use a hash map (or array for fixed alphabet) to store the last index of each character; update left pointer to max(left, lastIndex[char]+1) when a duplicate is encountered.
State that time complexity is O(n) since each character is visited at most twice, and space is O(min(n, m)) where m is the alphabet size.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.