GPU-Accelerated and Scalable Optimization (ScaleOpt)

Recent advancements in GPU-based large-scale optimization have been remarkable. Recognizing the revolution in optimizing neural network weights via large-scale GPU-accelerated algorithms, the optimization community has been interested in developing general purpose GPU-accelerated optimizers for various families of classic optimization problems, including linear programming, general conic optimization, combinatorial optimization, and more specific problem families such as flow optimization and optimal transport. Beyond deploying GPUs directly at classical problems, current frontier AI tools—including large language models (LLMs)—are being deployed to solve optimization problem. Various works have used neural networks to solve mixed integer problems, linear or quadratic programs, general combinatorial optimization problems, and more specific optimization problems such as LASSO and robust PCA. In this workshop, we aim to provide a platform for interested researchers to engage with each other on recent breakthroughs and current bottlenecks in designing large-scale GPU-based optimizers and synergizing AI systems with solving optimization problems.