I knew LC 128 cold so my first instinct was to just sort and scan, which works fine.
Clarify the problem constraints (e.g., subsequence vs. subarray, whether order matters, and if k is non-negative) before proposing a solution. Then, discuss a dynamic programming approach where you sort the array and use a data structure to efficiently find the longest valid subsequence ending at each element. Analyze time and space complexity, and consider edge cases.
Pro tip: Mention that sorting the array simplifies the problem because any valid subsequence can be rearranged into sorted order without affecting the adjacency condition. This insight often leads to a more efficient solution and shows you can reduce problem complexity.
Ask questions to confirm details: Is the subsequence required to maintain original order? Can k be negative? Are there duplicate values? What should be returned if the array is empty?
Discuss a naive O(2^n) approach to generate all subsequences, then explain why it's impractical and motivate the need for a more efficient algorithm.
Sort the array and use dynamic programming with a balanced BST or segment tree to find the longest subsequence ending at each element, considering previous elements within k.
State the time complexity (e.g., O(n log n)) and space complexity, and walk through edge cases like empty array, single element, or all elements within k.
Run through a small example to verify the logic, such as array [1, 3, 5, 7] with k=2, and show how the DP state updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The prompt left tree arity open, which I did not catch until I was halfway through writing a binary-tree solution.
Clarify the tree structure and node references, then propose a solution that finds the parent and depth of each node. Compare parents and depths to classify the relationship, discussing time/space trade-offs and edge cases.
Pro tip: Mention that you can optimize by stopping early if you find both nodes during a single traversal, and always handle edge cases like the same node or root nodes.
Ask about the tree type (binary, n-ary), whether parent pointers exist, and if node references are guaranteed to be in the tree. Confirm the definitions of siblings and cousins.
Decide between BFS (to track depth and parent level by level) or DFS (to find parents and depths recursively). Consider iterative vs recursive based on constraints.
During traversal, record each node's parent and depth. After traversal, compare: if parents equal and depths equal -> siblings; if depths equal but parents differ -> cousins; else neither.
State time complexity O(n) and space complexity O(n) for BFS/DFS. Discuss optimizations like early termination or using parent pointers if available.
Consider cases: same node, root and child, nodes at different depths, one node is ancestor of the other, and nodes not in tree. Explain how your solution handles them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.