I went straight for the brute force: take the shortest string, iterate over all its substrings, check each one against the rest.
Clarify the problem constraints (e.g., case sensitivity, empty list, no common substring) and then propose an efficient algorithm. A common optimal approach is to take the shortest string and use binary search on its length, checking for a common substring using a sliding window and a hash set. Alternatively, a trie or suffix automaton can be used, but binary search with rolling hash is simpler to implement and explain.
Pro tip: After presenting the solution, discuss trade-offs: for example, binary search with hashing is O(N*M*log(minLen)) but may have hash collisions; a trie approach is O(total characters) but uses more memory. Mention that in an interview, you'd start with a brute-force and then optimize, showing iterative thinking.
Ask about input size, character set, case sensitivity, and expected output if no common substring exists. Confirm whether the substring must be contiguous and whether multiple answers are possible.
Explain that you could generate all substrings of the shortest string and check each against all other strings, but this is O(N*M^2) and inefficient for large inputs.
Use binary search on the length of the common substring. For a given length L, check if any substring of length L from the shortest string appears in all others using a hash set for O(1) lookups. This reduces time to O(N*M*log(minLen)).
Mention that a trie built from all suffixes of the shortest string can find the longest common substring in O(total characters) but uses more memory. Compare with dynamic programming (for two strings) and suffix automaton for multiple strings.
State time and space complexity of your chosen solution. Walk through the given example to verify correctness. Mention potential issues like hash collisions and how to mitigate (e.g., double hashing).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.