Modeling and Optimization of Control Problems on GPUs

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 textttOptimalControl.jl into structured sparse nonlinear programs. These programs are compiled into GPU kernels using textttExaModels.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 textttMadNLP.jl and the GPU sparse linear solver cuDSS, yielding significant speed-ups over CPU-based approaches.