# Solution Alignment The prompt asks for an implementation-level answer. The safest way to present it is to define the state, maintain clear invariants, then walk through complexity and tests. ## Problem Restatement Compare common data structures and select appropriate ones for specific tasks. For arrays, linked lists, stacks, queues, hash tables, binary search trees (balanced and unbalanced), heaps, and graphs, state average and worst-case time/space complexities for search, insert, delete, and iteration; list key invariants; and provide one example where each excels and one where it performs poorly. ## Recommended Approach Choose traversal based on the required view or aggregate. DFS is natural for subtree computations and reconstruction; BFS is natural for level order or side views. Keep per-depth or per-position state when the output depends on columns, rows, or depths. ## Correctness The implementation should maintain an invariant after each loop or operation that directly matches the problem statement. At termination, that invariant implies the returned value has considered every valid candidate exactly once, or has preserved the required data-structure state after every API call. ## Complexity Most tree traversals are O(n) time and O(h) recursion stack for DFS or O(w) queue space for BFS, where h is height and w is maximum width. ## Edge Cases and Tests Empty tree, one node, skewed tree, duplicate values when reconstruction assumes uniqueness, deep recursion, and tie-breaking for same row/column nodes.