Modeling and Optimization of Control Problems on GPUs
Abstract
We present a fully Julia-based, GPU-accelerated workflow for solving large-scale sparse nonlinear optimal control problems. Continuous-time dynamics are modeled and then discretized via direct transcription with OptimalControl.jl into structured sparse nonlinear programs. These programs are compiled into GPU kernels using ExaModels.jl, leveraging SIMD parallelism for fast evaluation of objectives, constraints, gradients, Jacobians and Hessians. The resulting sparse problems are solved entirely on GPU using the interior-point solver MadNLP.jl and the GPU sparse linear solver cuDSS, yielding significant speed-ups over CPU-based approaches.
Turn this paper into a full lesson
ArcXiv compiles a staged curriculum from this paper: 8-12 lessons across beginner → advanced, synthesised section guides, visuals, flashcards, a quiz, exercises, and on-demand deep dives per section. Grounded in the abstract, never invented.