11 papers
Local LMO: Constrained Gradient Optimization via a Local Linear Minimization Oracle
Peter Richtárik, Kaja Gruntkowska, Hanmin Li
We design Local LMO - a new projection-free gradient-type method for constrained optimization. The key algorithmic idea is to replace the global linear minimization oracle over the…
Tighter Performance Theory of FedExProx
Wojciech Anyszka, Kaja Gruntkowska, Alexander Tyurin +1
We revisit FedExProx - a recently proposed distributed optimization method designed to enhance convergence properties of parallel proximal algorithms via extrapolation. In the proc…
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…
Drop-Muon: Update Less, Converge Faster
Kaja Gruntkowska, Yassine Maziane, Zheng Qu +1
Conventional wisdom in deep learning optimization dictates updating all layers at every step-a principle followed by all recent state-of-the-art optimizers such as Muon. In this wo…
Non-Euclidean Broximal Point Method: A Blueprint for Geometry-Aware Optimization
Kaja Gruntkowska, Peter Richtárik
The recently proposed Broximal Point Method (BPM) [Gruntkowska et al., 2025] offers an idealized optimization framework based on iteratively minimizing the objective function over…