I knew it was BFS immediately, which felt good, but then I wasted like two minutes debating whether to use a visited set or just mutate the grid in place.
Use BFS to explore the grid level by level, tracking the number of steps from the start. BFS guarantees the shortest path in an unweighted grid, and if the target is never reached, return -1.
Pro tip: Mention that BFS is optimal here because each move costs 1, and discuss early termination when the target is found to save time. Also, clarify edge cases like start equals target or blocked start/target.
Confirm grid dimensions, movement directions (4-directional), and that start and target are valid cells. Ask about edge cases like start == target or blocked cells.
Explain that BFS is ideal for finding the shortest path in an unweighted graph. Mention that DFS would not guarantee the shortest path.
Initialize a queue with the start cell and a visited set. For each cell, explore its 4 neighbors, mark them visited, and enqueue if valid and unvisited. Track steps by level.
Return the step count when the target is dequeued or enqueued. If the queue empties without reaching the target, return -1. Handle start == target by returning 0.
State that time complexity is O(R*C) since each cell is visited once, and space complexity is O(R*C) for the queue and visited set in the worst case.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.