Model the grid as a graph where each cell is a node, and use multi-source BFS starting from all initially rotten oranges simultaneously. Track the time level by level, counting minutes until no fresh oranges remain, and return the total time or -1 if any fresh orange is unreachable.
Pro tip: Explicitly discuss time and space complexity (O(m*n) time, O(m*n) space) and mention edge cases like an empty grid, no fresh oranges, or disconnected fresh oranges. This shows you think about efficiency and robustness, which Amazon values.
Restate the problem in your own words, confirm the rules (rot spreads to 4-directionally adjacent fresh oranges each minute), and ask clarifying questions about edge cases (e.g., empty grid, no fresh oranges, multiple rotten sources).
Explain that this is a shortest-path problem on an unweighted grid, so BFS is ideal. Emphasize that because rot spreads from multiple sources simultaneously, a multi-source BFS is needed.
Describe initializing a queue with all rotten oranges, tracking fresh count, and processing level by level. For each minute, process all nodes at the current level, rot adjacent fresh oranges, and increment time.
Explain how to detect if all oranges rot (fresh count reaches zero) and return the time, or if some remain fresh after BFS, return -1. Mention handling grids with no fresh oranges (return 0).
State the time complexity O(m*n) since each cell is visited once, and space complexity O(m*n) for the queue. Discuss potential optimizations like in-place modification of the grid to avoid extra space.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the problem as a shortest path on a graph where nodes are start, end, and all bus stations, with edge weights equal to Manhattan distances for walking and 0 for bus edges. Run Dijkstra's algorithm to find the minimum walking distance from start to end, then check if it is ≤ k.
Pro tip: Mention that you can optimize by only considering bus stations that are within k walking distance from start or end, and that using a priority queue ensures efficiency even with many stations.
Create nodes for start, end, and all bus stations. Add walking edges between every pair of nodes with weight equal to Manhattan distance, and add bus edges with weight 0 for each connection in the given bus graph.
Use Dijkstra's algorithm to compute the minimum total walking distance from start to end, treating bus rides as free.
Compare the computed minimum walking distance to k. If it is ≤ k, return true; otherwise, return false.
If the number of stations is large, consider pruning stations that are farther than k from both start and end, or use A* with a heuristic based on Manhattan distance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.