The interviewer simplified it partway through which honestly helped me get unstuck.
Start by clarifying the problem constraints (e.g., movement allowed, obstacles, matrix size) and then explain that BFS is optimal for finding the shortest path in an unweighted grid. Walk through the algorithm: initialize a queue with the source, track visited cells, and explore level by level until the target is reached, returning the distance.
Pro tip: Mention that you can optimize space by marking visited cells in-place (e.g., setting them to a special value) if the matrix is mutable, and discuss how to handle edge cases like unreachable target or source equals target.
Ask about movement directions (4 or 8), obstacles, matrix dimensions, and whether the target is guaranteed reachable. Confirm that each step moves to an adjacent cell.
Explain that BFS explores level by level, guaranteeing the shortest path in an unweighted graph. Contrast with DFS which may not find the shortest path.
Initialize a queue with the source cell and a distance counter. Use a visited set or modify the matrix to mark visited cells. Process nodes level by level, incrementing distance after each level.
Check if source equals target (return 0), if target is unreachable (return -1), and analyze time and space complexity: O(R*C) time and O(R*C) space in worst case.
Mention bidirectional BFS for faster search, or using a distance matrix if the matrix cannot be modified. Also note how to handle obstacles by treating them as blocked cells.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.