1 citations · 2 across the 13 of their papers we have counts for
13 papers
Broximal Gradient Descent: A Projection-Free Sister of Projected Gradient Descent
Peter Richtárik, Kaja Gruntkowska, Hanmin Li
We propose Broximal Gradient Descent (BroxGD), a projection-free sister method to projected gradient descent for constrained optimization. Its forward--backward construction replac…
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…
Error Feedback for Muon and Friends
Kaja Gruntkowska, Alexander Gaponov, Zhirayr Tovmasyan +1
Recent optimizers like Muon, Scion, and Gluon have pushed the frontier of large-scale deep learning by exploiting layer-wise linear minimization oracles (LMOs) over non-Euclidean n…
Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs)
Artem Riabinin, Egor Shulgin, Kaja Gruntkowska +1
Recent developments in deep learning optimization have brought about radically new algorithms based on the Linear Minimization Oracle (LMO) framework, such as and $\sf S…
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…
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…
Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations
Alexander Tyurin, Kaja Gruntkowska, Peter Richtárik
In practical distributed systems, workers are typically not homogeneous, and due to differences in hardware configurations and network conditions, can have highly varying processin…
Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity
Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik
Effective communication between the server and workers plays a key role in distributed optimization. In this paper, we focus on optimizing the server-to-worker communication, uncov…
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin +2
Byzantine robustness is an essential feature of algorithms for certain distributed optimization problems, typically encountered in collaborative/federated learning. These problems…
EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression
Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik
In this work we focus our attention on distributed optimization problems in the context where the communication time between the server and the workers is non-negligible. We obtain…