Classic trie plus DFS backtracking problem.
Model the board as a graph and use DFS with backtracking to explore all possible paths for each word, but optimize by building a trie of the word list to prune searches early. Start from each cell and traverse adjacent cells, marking visited cells to avoid reuse, and collect words that match trie prefixes.
Pro tip: Mention that you can further optimize by removing words that are prefixes of others or by sorting words by length, and discuss trade-offs between time and space complexity. Also, highlight the importance of early termination when no words remain to be found.
Ask about board dimensions, word list size, character set, and whether words can be reused. Confirm that each cell can be used at most once per word and that adjacency is 4-directional.
Decide to use a trie to store the word list for efficient prefix matching, and a visited matrix or in-place marking to track cell usage during DFS.
Outline a DFS backtracking approach: for each cell, if its character matches a trie child, recurse to adjacent cells, marking visited, and if a word end is reached, add to results. Prune branches when no trie node exists.
Discuss time complexity O(M*N*4^L) worst-case but improved by trie pruning, and space O(total characters in words). Mention optimizations like removing found words from trie to avoid duplicates.
Walk through examples: single cell, no words, words longer than board, overlapping paths. Ensure code handles empty board or word list.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.