BFS by level and just grab the last node at each level.
Clarify that the right side view consists of the rightmost node at each depth. Use a level-order traversal (BFS) and record the last node of each level, or a depth-first traversal (DFS) that prioritizes the right child and records the first node seen at each depth. Discuss trade-offs between BFS and DFS in terms of space and simplicity.
Pro tip: Mention that BFS uses O(width) space while DFS uses O(height) space, and that DFS can be more memory-efficient for skewed trees. Also, note that the problem can be solved with a simple recursive DFS that tracks depth, which is often cleaner to implement in an interview.
Confirm that the right side view includes the rightmost node at each level, and that nodes are listed from top to bottom. Ask if the tree can be empty or have only one node.
Decide between BFS (level-order traversal) and DFS (right-first traversal). Explain the trade-offs: BFS is intuitive but uses more space for wide trees; DFS uses less space for deep trees but requires tracking depth.
For BFS: use a queue, process each level, and add the last node's value to the result. For DFS: recursively traverse right subtree first, then left, and add the first node encountered at each depth to the result.
State that both approaches visit each node once, so time complexity is O(n). Space complexity is O(w) for BFS (w = max width) and O(h) for DFS (h = height), which is O(n) in the worst case.
Walk through a simple example (e.g., a tree with nodes 1,2,3,null,5,null,4) to verify the output [1,3,4]. Also consider edge cases: empty tree, single node, left-skewed tree.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem constraints (e.g., array size, element range, negative numbers) and then propose an efficient solution using a hash map to store prefix sums. Explain that for each prefix sum, you check if (prefix sum - k) exists in the map, which indicates a subarray summing to k. Walk through a small example to demonstrate correctness and analyze time and space complexity.
Pro tip: Mention that this approach handles negative numbers and zeros, unlike sliding window, and emphasize that you're optimizing from O(n^2) to O(n). Also, discuss edge cases like empty array or k=0 to show thoroughness.
Ask about input constraints: array size, element range, whether elements can be negative, and if the array is sorted. Confirm that subarrays must be contiguous and non-empty.
Mention that a naive O(n^2) approach checks all subarrays, which is inefficient for large inputs. This sets the stage for optimization.
Explain that a prefix sum is the cumulative sum up to an index. By storing prefix sums in a hash map with their frequencies, you can quickly find how many previous prefix sums equal current prefix sum minus k.
Initialize a hash map with {0:1} to handle subarrays starting at index 0. Iterate through the array, updating the prefix sum, and for each element, add the count of (prefix sum - k) from the map to the result, then increment the count of the current prefix sum.
State that time complexity is O(n) and space is O(n) in the worst case. Discuss edge cases: empty array, k=0, negative numbers, and large sums causing integer overflow.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.