Use a sliding window with two pointers to expand and contract the window while tracking character frequencies. Maintain a count of matched characters to efficiently determine when the window contains all pattern characters, and update the minimum window whenever a valid window is found.
Pro tip: Clarify edge cases upfront (e.g., pattern longer than string, empty inputs) and discuss time/space complexity (O(n) time, O(k) space) to demonstrate thoroughness. Mention that this is the classic 'Minimum Window Substring' problem and that the sliding window approach is optimal.
Ask clarifying questions about input constraints, character set (ASCII/Unicode), case sensitivity, and expected output if no valid window exists. Confirm that the window must contain all characters of the pattern, including duplicates.
Use a frequency map (e.g., array or hash map) for the pattern and a dynamic frequency map for the current window. Maintain a 'matched' counter to track how many pattern characters are fully satisfied.
Initialize left and right pointers at 0. Expand the window by moving right, updating the window frequency map and matched counter. When matched equals the number of unique characters in the pattern, a valid window is found.
While the window is valid, update the minimum window if the current length is smaller. Then contract from the left by moving left forward, updating the window frequency map and matched counter, until the window is no longer valid.
After the right pointer reaches the end, return the minimum window substring (or empty string if none). State that time complexity is O(n) and space complexity is O(k), where n is the string length and k is the number of unique characters in the pattern.
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