6 papers · 1 filter
Self-Certifying Primal-Dual Optimization Proxies for Large-Scale Batch Economic Dispatch
Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck
Recent research has shown that optimization proxies can be trained to high fidelity, achieving average optimality gaps under 1% for large-scale problems. However, worst-case analys…
Constraint-Informed Active Learning for End-to-End ACOPF Optimization Proxies
Miao Li, Michael Klamkin, Pascal Van Hentenryck +2
This paper studies optimization proxies, machine learning (ML) models trained to efficiently predict optimal solutions for AC Optimal Power Flow (ACOPF) problems. While promising,…
PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow
Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck
Machine Learning (ML) techniques for Optimal Power Flow (OPF) problems have recently garnered significant attention, reflecting a broader trend of leveraging ML to approximate and/…
DualSchool: How Reliable are LLMs for Optimization Education?
Michael Klamkin, Arnaud Deza, Sikai Cheng +2
Consider the following task taught in introductory optimization courses which addresses challenges articulated by the community at the intersection of (generative) AI and OR: gener…
Dual Interior Point Optimization Learning
Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck
In many practical applications of constrained optimization, scale and solving time limits make traditional optimization solvers prohibitively slow. Thus, the research question of h…
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…