新算法RXTX加速矩阵乘其转置，效率比现有方法提升5%。
$XX^{t}$ Can Be Faster
- 结合机器学习搜索与组合优化发现新算法
- 在大中小矩阵上均减少5%乘法和总运算量
- 适合需要高效矩阵计算的科研与工程场景
我们提出RXTX，一种用于计算矩阵与其转置乘积XX^T的新算法，其中X∈ℝ^(n×m)。该算法比当前最先进方法减少5%的乘法次数和5%的总运算量（加法与乘法）。加速效果不仅在大型矩阵（n→∞）下成立，也适用于小矩阵，包括n=4的情况。该算法通过将基于机器学习的搜索方法与组合优化相结合而发现。
原文摘要 · Abstract (English)
We present RXTX, a new algorithm for computing the product of matrix by its transpose $XX^{t}$ for $X\in \mathbb{R}^{n\times m}$. 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 $n \rightarrow \infty$, but also for small matrices including $n = 4$. The algorithm was discovered by combining Machine Learning-based search methods with Combinatorial Optimization.
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