1 citations · 1 across the 3 of their papers we have counts for
3 papers
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…
Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks
Andrew Rosemberg, Mathieu Tanneau, Bruno Fanzeres +2
The Optimal Power Flow (OPF) problem is integral to the functioning of power systems, aiming to optimize generation dispatch while adhering to technical and operational constraints…