I went straight to the classic recursive LCA approach and then realized mid-explanation that pointers were off the table.
Clarify that 'without pointers' means avoiding explicit pointer-based node structures, then propose an array-based representation where each node stores its parent index. Use this to compute the LCA by finding the intersection of ancestor paths or by using depth and parent arrays to climb up the tree.
Pro tip: Mention that this approach is common in competitive programming and embedded systems where memory layout matters, and highlight the trade-off between O(n) preprocessing and O(1) query time with binary lifting.
Confirm that 'without pointers' means no explicit pointer-based node objects, and that the tree is static or can be preprocessed. Ask about the expected query frequency and memory constraints.
Represent the tree using arrays: parent[], depth[], and optionally children lists. This avoids pointers and allows index-based navigation.
Compute depth for each node via BFS/DFS from root. Optionally build binary lifting table (up[k][v]) for O(log n) queries, or just use parent pointers for O(n) per query.
For two nodes, equalize depths by moving the deeper node up using parent array. Then move both up simultaneously until they meet. Return the meeting node.
Discuss time/space complexity: O(n) preprocessing, O(log n) per query with binary lifting vs O(n) per query with simple parent climbing. Mention memory overhead of binary lifting table.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.