I initially coded the straightforward version where deeper elements get more weight, which is the classic problem.
First, clarify the problem and edge cases, then explain that you need to find the maximum depth before computing the weighted sum. Use a recursive DFS to compute max depth, then a second DFS to accumulate the weighted sum, or combine both in one pass by collecting values and depths.
Pro tip: Mention that you can avoid two passes by storing (value, depth) pairs during a single traversal, then compute maxDepth and sum afterward—this shows optimization awareness. Also, discuss handling large inputs iteratively to avoid stack overflow, which is crucial for mobile environments.
Restate the problem to ensure understanding, ask about constraints (e.g., list size, depth limits, integer range), and confirm the weight formula.
Decide between recursive DFS (simpler) or iterative BFS/DFS with a stack (safer for deep nesting). Explain the trade-offs.
Traverse the nested list to find the maximum depth. This can be done in the same pass as collecting values or separately.
Traverse again (or use stored data) to compute the sum, applying weight = maxDepth - depth + 1 for each integer.
State time and space complexity (O(n) time, O(d) space for recursion). Discuss possible optimizations like single-pass or iterative approach.
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