Sparse Matrix Multiplication
A medium-tier problem at 69% community acceptance, tagged with Array, Hash Table, Matrix. Reported in interviews at Pinterest and 2 others.
Sparse Matrix Multiplication shows up in the OAs at Pinterest, LinkedIn, and TikTok, and it's one of those problems that looks straightforward until you realize the naive approach tanks on large inputs with mostly zeros. You're given two matrices, need to multiply them, but the trick is recognizing that most cells are empty so you don't waste cycles computing zeros. The 69% acceptance rate means most people solve it, but they solve it the slow way first. If you hit this during your assessment and blank on the optimization, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Sparse Matrix Multiplication"
Sparse Matrix Multiplication is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share.
Get StealthCoderThe obvious approach is standard matrix multiplication: three nested loops, O(n^3) time. It fails because if your matrices are 300x300 with only a handful of non-zero values, you're still computing hundreds of thousands of pointless products. The smart move is to use a Hash Table to store only the non-zero cells, iterate through rows and columns of the sparse representation, and skip zeros entirely. That cuts your work from cubic to proportional to the actual data density. Most candidates either don't see this or implement it sloppily with bad index management. When you're in the OA and the solution times out on test case 5, there's no time to rethink. StealthCoder is your hedge: it spots the hash table approach and hands you a working implementation before you panic.
Pattern tags
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Sparse Matrix Multiplication recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Sparse Matrix Multiplication interview FAQ
Is this just standard matrix multiply with a hash table bolted on?+
Not quite. Standard multiply is O(n^3) regardless of sparsity. The sparse version uses a hash table to store only non-zero entries, then iterates through those entries instead of every cell. That shifts the complexity from cubic to linear in the number of non-zero values, which is why it actually matters. It's a pattern recognition problem, not a data structure swap.
Pinterest and LinkedIn ask this. How different are their versions?+
The core trick is the same across all three: recognize sparsity, use a hash table or similar structure to avoid dead computation. Variations differ in constraints (matrix size, sparsity guarantees), input format (nested lists vs. coordinate pairs), and whether they ask for further optimization like caching. The underlying idea doesn't change.
What's the biggest pitfall candidates hit?+
Implementing the three nested loops and assuming it's fast enough. Second pitfall: using the hash table correctly but iterating wrong, like forgetting to check both dimensions or accessing indices out of order. Off-by-one errors in sparse indexing are common. Third: not thinking about space trade-offs. Hash table saves time but uses more memory.
Should I memorize this or understand the pattern?+
Understand the pattern. The insight is 'most cells are zero, so don't compute them.' Once that clicks, the code writes itself: store non-zeros in a map, loop through rows of first matrix and columns of second, check the map before multiplying. If you memorize code syntax and the pattern shifts slightly in the OA, you're stuck.
How does this relate to Hash Table and Array topics?+
Arrays are your input format. Hash Tables are where the optimization lives, mapping matrix indices to non-zero values so you skip zeros on the fly. The problem tests whether you can spot when a standard array algorithm fails and switch to a hash-based approach. That's a concrete skill both topics measure.
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