I went straight for a hashmap to track last-seen indices and slid a window of size K across the array.
Clarify the problem constraints and edge cases, then propose an efficient solution using a hash map to track the last seen index of each element. Explain that while iterating, if the current element was seen within the last K indices, return true; otherwise update its last seen index. Analyze time and space complexity, and discuss potential optimizations or alternative approaches.
Pro tip: Mention that the hash map approach is optimal for a single query, but if multiple queries with different K are expected, consider preprocessing or using a sliding window with a set. Also, explicitly handle edge cases like K=0 or empty array to show thoroughness.
Restate the problem in your own words and ask clarifying questions about constraints, such as whether K can be 0, if the array can be empty, and if T is relevant (since the problem only checks equality).
Acknowledge the naive O(n*K) approach of checking each element against the next K elements, and explain why it's inefficient for large inputs.
Describe the hash map approach: iterate through the array, store the last seen index of each element, and check if the current index minus the stored index is <= K. If so, return true; else update the stored index.
State that the time complexity is O(n) and space complexity is O(min(n, K)) or O(n) in the worst case, and explain why this is optimal.
Walk through edge cases like K=0, empty array, or no duplicates, and mentally test the solution with a small example to ensure correctness.
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