8 papers · 1 filter
BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training
Laurent Condat, Artavazd Maranjyan, Peter Richtárik
Slow and costly communication is often the main bottleneck in distributed optimization, especially in federated learning where it occurs over wireless networks. We introduce BiCoLo…
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…
Local SGD and Federated Averaging Through the Lens of Time Complexity
Adrien Fradin, Peter Richtárik, Alexander Tyurin
We revisit the classical Local SGD and Federated Averaging (FedAvg) methods for distributed optimization and federated learning. While prior work has primarily focused on iteration…
Ringleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity
Artavazd Maranjyan, Peter Richtárik
Asynchronous stochastic gradient methods are central to scalable distributed optimization, particularly when devices differ in computational capabilities. Such settings arise natur…
Convergence Analysis of the ProbAbilistic Gradient Estimator Algorithm for Weakly Convex Finite-Sum Optimization
Laurent Condat, Peter Richtárik
The ProbAbilistic Gradient Estimator algorithm (PAGE), a stochastic algorithm introduced by Li et al. in 2021, was designed to find stationary points for the average of smooth nonc…
The Stochastic Multi-Proximal Method for Nonsmooth Optimization
Laurent Condat, Elnur Gasanov, Peter Richtárik
Stochastic gradient descent type methods are ubiquitous in machine learning, but they are only applicable to the optimization of differentiable functions. Proximal algorithms are m…
Revisiting Stochastic Proximal Point Methods: Generalized Smoothness and Similarity
Zhirayr Tovmasyan, Grigory Malinovsky, Laurent Condat +1
The growing prevalence of nonsmooth optimization problems in machine learning has spurred significant interest in generalized smoothness assumptions. Among these, the (L0, L1)-smoo…
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…