Dot Product of Two Sparse Vectors
A medium-tier problem at 90% community acceptance, tagged with Array, Hash Table, Two Pointers. Reported in interviews at Nvidia and 4 others.
Sparse vectors show up in machine learning pipelines, recommendation systems, and anywhere you're processing high-dimensional data with mostly zeros. Nvidia, Meta, Pinterest, LinkedIn, and General Motors all ask this one. The trap is naive: if you iterate both vectors element-by-element, you'll time out on vectors with millions of dimensions and only a few non-zero values. The trick is recognizing that you only need to touch the non-zero entries, then matching them across both vectors efficiently. With an 89% acceptance rate, most people solve it, but the optimization gap between a brute force answer and a clean one separates candidates. If you hit this in a live OA and blank on the matching strategy, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Dot Product of Two Sparse Vectors"
Dot Product of Two Sparse Vectors 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. Made by an Amazon engineer who watched the leaked-problem repo become an industry secret. He decided you should have it too.
Get StealthCoderThe core insight is that you're computing a dot product by summing products of matching indices. Since most entries are zero, you can store only the non-zero values as index-value pairs using a hash table or sparse representation. The real problem becomes: given two lists of (index, value) pairs, find all indices that appear in both and multiply their values. Two pointers works cleanly if both sparse vectors are pre-sorted by index. Hash table lookup works too if you store one vector's non-zero entries in a map, then iterate the other and check for matches. The common mistake is ignoring sparsity and treating it like a dense array problem, which fails on large dimensions. This is a design question dressed up as an array problem. You're not just solving dot product; you're designing a sparse vector class that can handle real-world constraints. StealthCoder hedges the case where the optimal structure doesn't click during the assessment.
Pattern tags
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Dot Product of Two Sparse Vectors 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. Made by an Amazon engineer who watched the leaked-problem repo become an industry secret. He decided you should have it too. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Dot Product of Two Sparse Vectors interview FAQ
Is this problem actually about math or about picking the right data structure?+
Both, but structure is 80% of it. The math is trivial: multiply matching entries and sum them. The real work is avoiding iterations over zeros. Hash table or sorted pairs with two pointers are the patterns. Understanding which fits your input constraints matters more than the dot product formula itself.
Do I need to handle negative indices or values?+
The problem doesn't restrict sign, so assume they're possible. Your hash table or index-value pairs work fine with negatives. The algorithm doesn't care about sign; it only cares about matching indices and multiplying values, whatever they are.
Why do Nvidia and Meta ask this if 90% of people pass it?+
High acceptance rate means most people get a working solution, but follow-ups trip candidates up. They'll ask you to optimize space, handle updates to the vector, or compute dot product for many vectors at once. The base problem is a gateway to design thinking.
Should I sort the sparse vectors or use a hash table?+
Hash table is simpler to code and doesn't require pre-sorting, so it's faster to implement in an OA. Two pointers is more elegant if both vectors are already sorted by index. Pick hash table unless the interviewer explicitly says the vectors are sorted.
Does this pattern show up in other LeetCode problems?+
Yes. Any problem involving matching or combining data from two sparse collections benefits from the same trick. Merge sorted arrays, intersection of two arrays, and similar problems use the same two-pointer or hash-table logic.
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