4 papers
Broximal Alignment for Global Non-Convex Optimization
Kaja Gruntkowska, Hanmin Li, Xun Qian +1
Most non-convex optimization theory is built around gradient dynamics, leaving global convergence largely unexplored. The dominant paradigm focuses on stationarity, certifying only…
Stabilized Proximal Point Method via Trust Region Control
Hanmin Li, Kaja Gruntkowska, Peter Richtárik
The Proximal Point Method (PPM) (Rockafellar, 1976) is a fundamental tool for nonsmooth convex optimization. However, its convergence is not linear under general convexity in the a…
The Ball-Proximal (="Broximal") Point Method: a New Algorithm, Convergence Theory, and Applications
Kaja Gruntkowska, Hanmin Li, Aadi Rane +1
Non-smooth and non-convex global optimization poses significant challenges across various applications, where standard gradient-based methods often struggle. We propose the Ball-Pr…
On the Convergence of FedProx with Extrapolation and Inexact Prox
Hanmin Li, Peter Richtárik
Enhancing the FedProx federated learning algorithm (Li et al., 2020) with server-side extrapolation, Li et al. (2024a) recently introduced the FedExProx method. Their theoretical a…