3 papers
math.OC2026
Self-Supervised Learning of Parametric Approximation for Security-Constrained DC-OPF
Anderson Anrrango, André Quisaguano, Gonzalo E. Constante-Flores +1
This paper introduces a self-supervised learning framework for approximating the Security-Constrained DC Optimal Power Flow (SC-DCOPF) problem using a parametric linear model. The…
cs.LG2025
Enforcing Hard Linear Constraints in Deep Learning Models with Decision Rules
Gonzalo E. Constante-Flores, Hao Chen, Can Li
Deep learning models are increasingly deployed in safety-critical tasks where predictions must satisfy hard constraints, such as physical laws, fairness requirements, or safety lim…
math.OC2025
A Quadratically-Constrained Convex Approximation for the AC Optimal Power Flow
Gonzalo E. Constante-Flores, Can Li
We introduce a quadratically-constrained approximation (QCAC) of the AC optimal power flow (AC-OPF) problem. Unlike existing approximations like the DC-OPF, our model does not rely…