2 citations · 2 across the 4 of their papers we have counts for
6 papers
Using Non-Lipschitz Signum-based Functions for Distributed Optimization and Machine Learning: Trade-off Between Con-vergence Rate and Optimality Gap
Mohammadreza Doostmohammadian, Amir Ahmad Ghods, Alireza Aghasi +2
In recent years, the prevalence of large-scale data-sets and the demand for sophisti-cated learning models have necessitated the development of efficient distributed ma-chine learn…
Machine Learning and CPU (Central Processing Unit) Scheduling Co-Optimization over a Network of Computing Centers
Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, Hamid R. Rabiee
In the rapidly evolving research on artificial intelligence (AI) the demand for fast, computationally efficient, and scalable solutions has increased in recent years. The problem o…
Momentum-based Distributed Resource Scheduling Optimization Subject to Sector-Bound Nonlinearity and Latency
Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, Hamid R. Rabiee
This paper proposes an accelerated consensus-based distributed iterative algorithm for resource allocation and scheduling. The proposed gradient-tracking algorithm introduces an au…
Nonlinear Perturbation-based Non-Convex Optimization over Time-Varying Networks
Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, Hamid R. Rabiee
Decentralized optimization strategies are helpful for various applications, from networked estimation to distributed machine learning. This paper studies finite-sum minimization pr…
A Fully Adaptive Steepest Descent Method
Z. R. Gabidullina
For solving pseudo-convex global optimization problems, we present a novel fully adaptive steepest descent method (or ASDM) without any hard-to-estimate parameters. For the step-si…
The Minkowski Difference for Convex Polyhedra and Some its Applications
Z. R. Gabidullina
The aim of the paper is to develop a unified algebraical approach to representing the Minkowski difference for convex polyhedra. Namely, there is proposed an exact analytical formu…