I started with a 2D array for the board and a separate boolean grid for revealed state, which felt clean at first.
Start by clarifying requirements and edge cases (e.g., first-click safety, win condition), then outline the data structures and algorithms before coding. Implement the board with a 2D array and a separate visibility state, use BFS/DFS for flood fill, and analyze complexity. Finally, walk through test cases covering normal play, edge cases, and performance.
Pro tip: Mention the first-click safety feature (guaranteeing the first click is never a bomb) as it shows attention to user experience and is a common Minesweeper requirement. Also, discuss how to handle large boards efficiently by using iterative BFS to avoid stack overflow.
Ask about board size limits, bomb density, first-click safety, win/lose conditions, and whether the board can be modified after initialization. This ensures you design the right solution.
Choose a 2D array (or list of lists) to represent the board, with each cell storing bomb status, adjacent bomb count, and visibility state. Consider using an enum for cell states (hidden, revealed, flagged).
Randomly place k bombs (ensuring first-click safety if required), compute adjacent bomb counts for all cells, and implement printBoard to return a string representation of the visible state.
Handle bomb hits (game over), reveal cells, and use BFS/DFS to recursively reveal connected zero-adjacent-bomb regions. Use a queue (BFS) or stack (DFS) to avoid recursion depth issues.
Discuss time and space complexity for initialization (O(m*n)), click (O(m*n) worst-case for flood fill), and printBoard (O(m*n)). Provide test cases for edge cases, bomb hits, flood fill, and win condition.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that the naive dense grid is infeasible due to memory, then propose a sparse representation (e.g., hash map of bombs) and lazy evaluation of cells. Walk through the click() algorithm: check if the clicked cell is a bomb, count neighboring bombs on-demand by querying the sparse structure, and only reveal/expand cells as needed. Discuss trade-offs between time and space, and mention caching and pruning optimizations.
Pro tip: Emphasize that the key insight is to treat the board as a sparse graph where only bomb cells and their neighbors matter; most cells are empty and can be generated on the fly. This shows you can identify the minimal state needed and avoid premature optimization.
Explain that a dense 2D array of size m x n is impossible for huge m and n, so we must avoid allocating memory for empty cells. The only essential data is the set of bomb locations.
Propose using a hash set or hash map to store bomb coordinates (e.g., key = row * n + col or a tuple). This gives O(1) average lookup for bomb presence and uses O(k) memory where k is the number of bombs.
For a click at (r, c), first check if it's a bomb (game over). If not, compute the number of adjacent bombs by checking the 8 neighbors against the bomb set. If count > 0, reveal just that cell; if count == 0, reveal it and recursively expand to neighbors, but only generate/reveal cells on demand.
Cache computed neighbor counts for revealed cells to avoid recomputation. Prune expansion by not revisiting already revealed cells and by stopping at cells with adjacent bombs. Optionally, use a union-find or flood-fill with a queue to handle large empty regions efficiently.
Compare time vs. space: sparse structures save memory but may have slower constant factors. Lazy evaluation reduces initial cost but may increase per-click latency. Caching speeds up repeated clicks but uses extra memory. Pruning avoids unnecessary work but complicates logic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.