3 papers
math.OC2026
Local LMO is Secretly a Projection Method!
Peter Richtárik, Ammar Mahran
The local linear minimization oracle (Ferris and Zavriev, 1996; arXiv:2605.08850), or Local LMO, solves constrained convex problems without having to compute a projection: it minim…
cs.LG2026
LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging
Yassine Maziane, Ammar Mahran, Artavazd Maranjyan +1
Communication is a major bottleneck in distributed learning, especially in large-scale settings and in federated learning environments with slow links. Three standard ways to reduc…
cs.LG2026
Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
Ammar Mahran, Artavazd Maranjyan, Peter Richtárik
Asynchronous stochastic gradient descent (ASGD) is a standard way to exploit heterogeneous compute resources in distributed learning: instead of forcing fast workers to wait for sl…