The individual operations aren't hard to code but keeping them straight under pressure is a different story.
Clarify the exact semantics of each operation, especially rotation, and confirm whether operations should be applied in-place or can return a new matrix. Then design a modular solution where each operation is a separate function, and consider optimizing by tracking row/column permutations and reversals instead of mutating the matrix for each instruction.
Pro tip: Mention that rotation can be decomposed into transpose and row reversal, and that you can avoid O(n*m) work per operation by maintaining lazy transformations (e.g., row/column order and reversal flags) and only materializing the final matrix when needed.
Ask about matrix dimensions, instruction format, whether operations are in-place, and how rotation should behave for non-square matrices. Confirm the expected output.
Implement each operation as a separate function: swap rows, swap columns, reverse row, reverse column, and rotate (transpose + reverse each row). This makes the code clean and testable.
Instead of mutating the matrix for each instruction, track row/column order and reversal flags. For rotation, update the orientation and adjust indices accordingly. This reduces time complexity.
For 90-degree rotation, note that transpose swaps rows and columns, and reversing each row completes the rotation. For lazy approach, update the mapping of logical to physical indices.
Walk through a small example, verify each operation, and test edge cases like 1x1 matrix, empty matrix, and multiple rotations. Discuss time and space complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.