61 citations · 166 across the 10 of their papers we have counts for
4 papers · 1 filter
Compact Optimization Learning for AC Optimal Power Flow
Seonho Park, Wenbo Chen, Terrence W. K. Mak +1
This paper reconsiders end-to-end learning approaches to the Optimal Power Flow (OPF). Existing methods, which learn the input/output mapping of the OPF, suffer from scalability is…
Bucketized Active Sampling for Learning ACOPF
Michael Klamkin, Mathieu Tanneau, Terrence W. K. Mak +1
This paper considers optimization proxies for Optimal Power Flow (OPF), i.e., machine-learning models that approximate the input/output relationship of OPF. Recent work has focused…
Spatial Network Decomposition for Fast and Scalable AC-OPF Learning
Minas Chatzos, Terrence W. K. Mak, Pascal Van Hentenryck
This paper proposes a novel machine-learning approach for predicting AC-OPF solutions that features a fast and scalable training. It is motivated by the two critical considerations…
Lagrangian Duality for Constrained Deep Learning
Ferdinando Fioretto, Pascal Van Hentenryck, Terrence WK Mak +3
This paper explores the potential of Lagrangian duality for learning applications that feature complex constraints. Such constraints arise in many science and engineering domains,…