Optimization for Classical Machine Learning Problems on the GPU
Keywords:Machine Learning (ML), Constraint Satisfaction And Optimization (CSO)
AbstractConstrained 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.
How to Cite
Laue, S., Blacher, M., & Giesen, J. (2022). Optimization for Classical Machine Learning Problems on the GPU. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7300-7308. https://doi.org/10.1609/aaai.v36i7.20692
AAAI Technical Track on Machine Learning II