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Clarify the constraints (e.g., obstacles, movement rules, cost) and then model the problem as a graph search. Choose BFS for unweighted shortest path or Dijkstra/A* for weighted, and discuss trade-offs like time/space complexity and optimality.
Pro tip: Always ask clarifying questions about edge cases (e.g., no path, source equals destination) and mention that you'd test with small matrices first. This shows attention to detail and prevents incorrect assumptions.
Ask about movement directions, obstacles, cost function, and whether the path must be shortest or just any valid path. Confirm input/output format and edge cases.
Based on constraints, select BFS for unweighted shortest path, Dijkstra for non-negative weights, or A* if a heuristic is available. For any path, DFS or BFS works.
Describe the steps: initialize a queue/priority queue, track visited cells, and explore neighbors while checking constraints. For weighted, maintain distance array.
State time and space complexity (e.g., O(V+E) for BFS, O(E log V) for Dijkstra). Discuss when to use each and potential optimizations like bidirectional search.
Mention edge cases: source/destination out of bounds, no path, multiple paths, large matrices. Suggest testing with small examples and verifying correctness.
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