Knew immediately it was a sliding window plus hashmap problem but kept fumbling the window size calculation.
Use a sliding window approach with a hash map to count word frequencies. Since all words are equal length, slide the window by word length and check each substring of that length against the word counts. Optimize by considering each possible starting offset modulo word length to avoid redundant checks.
Pro tip: Clarify edge cases upfront: empty string, empty word list, words with duplicates, and overlapping matches. Also, mention that the solution should handle large inputs efficiently, aiming for O(n) time where n is the string length.
Confirm that all words are equal length and that the concatenation must include every word exactly once. Discuss edge cases like empty inputs and duplicate words.
Use a hash map to store the frequency of each word in the list. This allows O(1) lookups and comparisons.
Iterate over each possible starting offset from 0 to wordLength-1. For each offset, slide a window of size totalWords*wordLength, updating word counts as you move.
Write code that maintains a count of matched words and a current window word frequency map. When the window size equals the total length, check if all words are matched and record the start index.
Explain that the time complexity is O(n) where n is the string length, as each character is processed a constant number of times. Test with examples including duplicates and overlapping matches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.