Start by clarifying the problem constraints and edge cases, then propose a divide-and-conquer strategy that uses group testing to identify bad nodes efficiently. Analyze the round and call complexity, and discuss trade-offs between the two.
Pro tip: Mention that this is a variant of group testing and that adaptive strategies can reduce calls, but non-adaptive may be needed for round efficiency. Also, note that identifying all bad nodes is equivalent to finding the complement of the set of good nodes.
Confirm that tests return true only if all nodes in the subset are good, and that tests in the same round must be disjoint. Ask about constraints on N and whether the number of bad nodes is known.
Propose a simple approach: test all nodes in pairs or small groups to identify good nodes, then deduce bad nodes. Analyze its round and call complexity.
Use a binary splitting strategy: recursively split the set of nodes into subsets, test each subset, and if a test returns false, recurse on that subset to find bad nodes. Ensure tests within a round are disjoint.
Derive the number of rounds and calls in terms of N and the number of bad nodes. Compare with the baseline and discuss trade-offs.
Consider noisy tests, unknown number of bad nodes, and how the algorithm scales. Mention potential applications in ML (e.g., data cleaning, ensemble methods).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.