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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…
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…
Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow
Andrew Rosemberg, Michael Klamkin, Pascal Van Hentenryck
The growing scale of power systems and the increasing uncertainty introduced by renewable energy sources necessitates novel optimization techniques that are significantly faster an…