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math.OC2025
Verifying Probabilistic Regions of Attraction with Neural Lyapunov Functions for Stochastic Systems
Yun Su, Hans De Sterck, Jun Liu
Leveraging a stochastic extension of Zubov's equation, we develop a physics-informed neural network (PINN) approach for learning a neural Lyapunov function that captures the larges…
math.OC2024
MGProx: A nonsmooth multigrid proximal gradient method with adaptive restriction for strongly convex optimization
Andersen Ang, Hans De Sterck, Stephen Vavasis
We study the combination of proximal gradient descent with multigrid for solving a class of possibly nonsmooth strongly convex optimization problems. We propose a multigrid proxima…