Rows and columns were fine, I had that part coded up pretty fast.
Start by clarifying the problem constraints and edge cases, then propose an efficient algorithm that scans each row, column, and diagonal once, using early termination to avoid unnecessary checks. Discuss time and space complexity, and consider whether the matrix is sparse or dense to optimize further.
Pro tip: Mention that you can precompute prefix sums or use bitwise operations to speed up checks, but always prioritize clarity and correctness first. Also, explicitly handle edge cases like empty matrix or single row/column.
Ask about matrix dimensions, whether it can be empty, and if diagonals of any length count or only full-length ones. Confirm the definition of 'main diagonal' and 'anti-diagonal'.
Decide to iterate through each row, column, and diagonal, checking for all true values. Use early exit as soon as a false is found to save time.
For each line, loop through its elements and break if any is false. If the loop completes, return true. Ensure diagonals are correctly indexed.
State that time complexity is O(m*n) since each cell is visited at most a constant number of times, and space is O(1). Mention possible optimizations like bitwise operations for boolean matrices.
Walk through a small example and discuss edge cases like all true, all false, single row/column, and non-square matrices to ensure correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.