The problem itself is not that hard once you see it clearly, but the format wrecked me a little.
Clarify the problem constraints and edge cases first, then propose an efficient algorithm such as binary search on the value range or a heap-based merge to find the smallest common element. Implement the solution with clean code and test it against your own test cases, including edge cases like empty matrix or no common element.
Pro tip: Demonstrate adaptability by discussing trade-offs between different approaches (e.g., binary search vs. heap) and how you would handle ambiguity in the problem statement, such as whether rows can be empty or contain duplicates.
Ask clarifying questions to understand constraints: matrix dimensions, row lengths, duplicates, empty rows, and expected return value. Identify edge cases like empty matrix, single row, or no common element.
Choose an approach: binary search on the value range (from min of first column to max of last column) or use a min-heap to merge rows. Consider time and space complexity.
Write clean, modular code with clear variable names. Handle edge cases explicitly and ensure the algorithm correctly finds the smallest common element.
Create test cases covering normal scenarios, edge cases (empty matrix, no common element, single row), and large inputs. Verify correctness and performance.
Explain the time and space complexity of your solution and compare with alternative approaches. Discuss potential optimizations or variations.
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