English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Constrained optimization problems arise frequently in classical machinelearning. There exist frameworks addressing constrained optimization, forinstance, CVXPY and GENO. However, in contrast to deep learning frameworks, GPUsupport is limited. Here, we extend the GENO framework to also solveconstrained optimization problems on the GPU. The framework allows the user tospecify constrained optimization problems in an easy-to-read modeling language.A solver is then automatically generated from this specification. When run onthe GPU, the solver outperforms state-of-the-art approaches like CVXPY combinedwith a GPU-accelerated solver such as cuOSQP or SCS by a few orders ofmagnitude.

Optimization for Classical Machine Learning Problems on the GPU