141 citations · 266 across the 15 of their papers we have counts for
6 papers · 1 filter
Optimal convergence rates of totally asynchronous optimization
Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian +1
Asynchronous optimization algorithms are at the core of modern machine learning and resource allocation systems. However, most convergence results consider bounded information dela…
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees
Hamid Reza Feyzmahdavian, Mikael Johansson
We introduce novel convergence results for asynchronous iterations that appear in the analysis of parallel and distributed optimization algorithms. The results are simple to apply…
Distributed learning with compressed gradients
Sarit Khirirat, Hamid Reza Feyzmahdavian, Mikael Johansson
Asynchronous computation and gradient compression have emerged as two key techniques for achieving scalability in distributed optimization for large-scale machine learning. This pa…
Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server
Arda Aytekin, Hamid Reza Feyzmahdavian, Mikael Johansson
This paper presents an asynchronous incremental aggregated gradient algorithm and its implementation in a parameter server framework for solving regularized optimization problems.…
An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization
Hamid Reza Feyzmahdavian, Arda Aytekin, Mikael Johansson
Mini-batch optimization has proven to be a powerful paradigm for large-scale learning. However, the state of the art parallel mini-batch algorithms assume synchronous operation or…
Global convergence of the Heavy-ball method for convex optimization
Euhanna Ghadimi, Hamid Reza Feyzmahdavian, Mikael Johansson
This paper establishes global convergence and provides global bounds of the convergence rate of the Heavy-ball method for convex optimization problems. When the objective function…