论文标题
在GPU上对经典机器学习问题的优化
Optimization for Classical Machine Learning Problems on the GPU
论文作者
论文摘要
经典的机器学习中经常出现受限的优化问题。存在解决受约束优化的框架，例如CVXPY和GENO。但是，与深度学习框架相反，GPU支持是有限的。在这里，我们将GENO框架扩展到也解决了GPU上受约束的优化问题。该框架允许用户在易于阅读的建模语言中指定受约束的优化问题。然后，从此规范自动生成求解器。当在GPU上运行时，求解器的表现要优于诸如CVXPY之类的最先进的方法，并结合了GPU加速求解器（例如CuosQP或SCS）的数量级。
Constrained optimization problems arise frequently in classical machine learning. There exist frameworks addressing constrained optimization, for instance, CVXPY and GENO. However, in contrast to deep learning frameworks, GPU support is limited. Here, we extend the GENO framework to also solve constrained optimization problems on the GPU. The framework allows the user to specify constrained optimization problems in an easy-to-read modeling language. A solver is then automatically generated from this specification. When run on the GPU, the solver outperforms state-of-the-art approaches like CVXPY combined with a GPU-accelerated solver such as cuOSQP or SCS by a few orders of magnitude.
