My first instinct was to just iterate over all blocks, track the min per block, check for the value 1, and keep a running global min.
Clarify the problem constraints and edge cases, then propose an efficient algorithm that scans the grid in 3x3 blocks, checks for invalid blocks containing 1, and tracks the minimum representative. Discuss time and space complexity, and consider trade-offs between different approaches.
Pro tip: Demonstrate awareness of edge cases such as grids with dimensions not divisible by 3, empty grids, or all blocks invalid. Also, mention that early termination or skipping invalid blocks can optimize performance.
Ask questions to confirm grid dimensions, data types, and what to return if no valid block exists. Ensure understanding of 'non-overlapping 3x3 blocks' and 'representative'.
Describe a systematic scan of the grid in steps of 3 rows and 3 columns. For each block, check if it contains a 1; if not, compute its minimum and compare with the current global minimum.
State that the algorithm visits each cell once, so time complexity is O(rows * cols) and space complexity is O(1) beyond input storage.
Discuss handling of grids where rows or columns are not multiples of 3 (ignore leftover cells), empty grids, and cases where all blocks are invalid (return a sentinel like null or -1).
Mention potential optimizations like early exit if a block's minimum is already greater than the current global minimum, or using sliding window techniques if the problem were extended.
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