XXt Can Be Faster
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Abstract (arXiv)
We present RXTX, a new algorithm for computing the product of matrix by its transpose XXt for X∈ ℝn× m. RXTX uses 5% fewer multiplications and 5% fewer operations (additions and multiplications) than State-of-the-Art algorithms. Note that the accelerations not only holds asymptotically for large matrices with n rightarrow ∞, but also for small matrices including n = 4. The algorithm was discovered by combining Machine Learning-based search methods with Combinatorial Optimization.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 25 references that are in the map. First posted on arXiv 14 May 2025.
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4 of 25 references.
References · 4
- 2018Shampoo: Preconditioned Stochastic Tensor Optimizationcited by 426
- 2021Learning Large Neighborhood Search Policy for Integer Programmingcited by 35
- 2016A network that learns Strassen multiplicationcited by 1
- 2021Efficiently Parallelizable Strassen-Based Multiplication of a Matrix by its Transposecited by 1
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