This is basically rotting oranges with a coat of paint.
Model the grid as a graph and use multi-source BFS starting from all initially infected cells simultaneously. Track the number of days (BFS levels) until no more cells can be infected, then check if all cells are infected; if not, return -1.
Pro tip: Clarify edge cases upfront, such as an empty grid or no initially infected cells, and discuss time/space complexity (O(M*N)) to demonstrate thoroughness.
Confirm the grid dimensions, infection spread rules, and what constitutes a 'day'. Discuss edge cases like empty grid, all cells initially infected, or no initially infected cells.
Recognize that simultaneous spread from multiple sources is naturally handled by multi-source BFS. Explain why BFS is optimal for finding the minimum time to reach all cells.
Initialize a queue with all initially infected cells and set their distance to 0. Process level by level, infecting healthy neighbors and incrementing the day count after each level.
After BFS, check if any healthy cells remain. If yes, return -1; otherwise, return the number of days (max distance from any source).
State that time and space complexity are O(M*N). Walk through a small example to verify correctness and discuss potential optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Cleaner than it sounds once you realize immune cells are just walls.
Model the grid as a graph and run a multi-source BFS from all initially infected cells, treating immune cells as blocked. After BFS, check if any healthy cell remains unvisited; if so, return -1, otherwise return the maximum distance reached.
Pro tip: Clarify edge cases upfront: what if there are no healthy cells initially? What if immune cells completely isolate a region? Also, mention that you can optimize by tracking the count of infected cells to early-exit if all reachable cells are infected.
Ask about grid size, movement directions (4 or 8), and whether immune cells are static. Confirm that 'reachable' means via 4-directional adjacency and that immune cells block infection.
Represent each cell as a node; edges connect adjacent non-immune cells. Use multi-source BFS from all initially infected cells to compute the minimum time to infect each healthy cell.
Initialize a queue with all infected cells at day 0. While queue is not empty, pop a cell, and for each non-immune, uninfected neighbor, mark infected, set distance = current distance + 1, and enqueue. Track the maximum distance.
After BFS, iterate through all cells. If any healthy cell remains uninfected, return -1. Otherwise, return the maximum distance recorded.
Time complexity is O(N*M) since each cell is processed once. Space is O(N*M) for the queue and visited set. Mention early termination if all healthy cells are infected before BFS completes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where things get genuinely annoying.
Model the infection and recovery using a multi-source BFS where each cell tracks its infection start day, and when a cell reaches D days infected, it becomes immune and stops spreading. Simulate day by day, updating states and counting active infections until none remain, returning the total days elapsed.
Pro tip: Clarify upfront whether D is inclusive (e.g., infected for D days means it recovers at the start of day D+1) and whether immunity is permanent; this avoids off-by-one errors and shows attention to detail.
Confirm the meaning of 'infected for D days', whether recovery happens at the start or end of a day, and if immunity is permanent. Also discuss grid boundaries, initial infected cells, and whether multiple waves can occur.
Use a queue for BFS to process newly infected cells, a 2D array to track each cell's state (healthy, infected, immune) and infection start day, and a counter for active infections.
For each day, first process recoveries: any cell infected for D days becomes immune and is removed from active infections. Then spread infection from currently infected cells to healthy neighbors, updating their state and start day.
Increment a day counter each simulation step, and stop when the active infection count reaches zero. Return the total days elapsed.
Discuss time complexity O(N*M) where N and M are grid dimensions, and space complexity O(N*M). Mention alternative approaches like event-driven simulation if D is large, and trade-offs between clarity and efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Didn't get here personally but from what I've heard there are at least three different versions of this part floating around depending on who's running your loop.
Clarify the variant (threshold infection or death countdown) and define the grid and neighbor rules. Then outline a simulation loop that tracks state changes and counts, using appropriate data structures for efficiency. Finally, discuss how to return the required counts or timings and analyze trade-offs.
Pro tip: Demonstrate awareness of performance by suggesting optimizations like using a queue for active cells or parallelizing updates, and mention edge cases such as K=0 or K greater than the number of neighbors.
Ask clarifying questions about grid size, neighbor definition (e.g., Moore or von Neumann), K value, and whether multiple variants should be supported. Confirm what counts or timings need to be returned.
Define cell states (healthy, infected, dead) and transition rules. For threshold infection, a healthy cell becomes infected if infected neighbors >= K. For death countdown, infected cells with infected neighbors >= K start a countdown and die after a set number of steps.
Use a 2D array for the grid and maintain counts of infected neighbors, possibly with a secondary array. For efficiency, consider updating only cells whose neighbor counts change, using a queue or set of active cells.
Iterate over time steps, applying rules simultaneously (using a copy or double buffer). Track relevant metrics such as total infected over time, time to reach steady state, or number of deaths per step.
Return the required counts or timings based on the variant. Discuss time/space complexity and potential optimizations, and mention how the approach scales with grid size and K.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem: the grid has cells with death counts, and each day you choose a row or column to eliminate all remaining cells in it. The goal is to minimize total deaths. This is a combinatorial optimization problem; likely NP-hard, so discuss greedy strategies, dynamic programming for small grids, or integer programming formulations. Also consider if the grid is fully known and if choices are adaptive.
Pro tip: Show awareness that this is a variant of the maximum coverage problem or set packing, and that a greedy heuristic (e.g., pick the row/column with the highest sum of remaining deaths) often performs well but may not be optimal. Mention that for small grids, you can solve it exactly with DP over subsets of rows/columns.
Ask about the grid size, whether death counts are known in advance, and if choices are adaptive. Confirm that eliminating a row/column removes all remaining cells in it, and that the goal is to minimize total deaths.
Recognize this as a combinatorial optimization problem similar to maximum coverage or set packing. Note that it is likely NP-hard, so exact solutions may be infeasible for large grids.
For small grids (e.g., up to 20 rows/columns), use dynamic programming over subsets of rows and columns, or integer programming, to find the optimal sequence.
For large grids, suggest a greedy heuristic: repeatedly choose the row or column with the highest sum of remaining deaths. Discuss its approximation ratio and potential improvements like local search.
Compare the greedy approach to other heuristics (e.g., random, simulated annealing) and discuss trade-offs between optimality and computational efficiency. Mention that the greedy approach is simple and often effective.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.