GPU acceleration can make general-purpose optimization solvers orders of magnitude faster than traditional solvers, while remaining flexible enough for domain-specific customization through a Python interface.
cuGenOpt is a GPU-accelerated framework for solving combinatorial optimization problems (like routing and scheduling) that balances generality, speed, and ease of use. It uses CUDA to run multiple solution attempts in parallel, lets experts add custom solvers, and includes an AI assistant that converts plain-English problem descriptions into working code.