  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.
