Use a sliding window with a hash map to track the last seen index of each element, expanding the right pointer and moving the left pointer when a duplicate is found. This yields an O(n) time and O(min(n, k)) space solution, where k is the size of the character set or alphabet. Clearly explain the invariant that the window always contains unique elements.
Pro tip: After presenting the optimal solution, mention the brute-force O(n^2) approach and why it's inefficient, then discuss trade-offs like using a set vs. a map (e.g., set requires shrinking one by one, while map allows jumping). This shows you consider multiple solutions and optimize thoughtfully.
Confirm that the input is a sequence (e.g., string or list) and that we need the length of the longest contiguous sublist without repeated elements. Ask about edge cases like empty input or all unique elements.
Mention that a naive solution checks all sublists for uniqueness, which is O(n^3) or O(n^2) with optimization. Explain why it's inefficient for large inputs.
Explain that we maintain a window [left, right] and a map storing the last index of each element. When a duplicate is found, move left to max(left, last_index + 1). Update the max length at each step.
Trace the algorithm on a small example like 'abcabcbb' to demonstrate how the window and map update, and how the maximum length is computed.
State that time complexity is O(n) because each element is processed once, and space is O(min(n, k)) where k is the number of distinct elements. Discuss handling empty input and Unicode characters.
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