2 papers
cs.LG2025
A General and Streamlined Differentiable Optimization Framework
Andrew W. Rosemberg, Joaquim Dias Garcia, François Pacaud +5
Differentiating through constrained optimization problems is increasingly central to learning, control, and large-scale decision-making systems, yet practical integration remains c…
cs.LG2025
Sobolev Training of End-to-End Optimization Proxies
Andrew W. Rosemberg, Joaquim Dias Garcia, Russell Bent +1
Optimization proxies - machine learning models trained to approximate the solution mapping of parametric optimization problems in a single forward pass - offer dramatic reductions…