4 citations · 4 across the 4 of their papers we have counts for
5 papers
Budget-Constrained Multi-Consensus Decentralized Gradient Descent
Shuyi Ren, Nicol`o Michelusi, Erik G. Larsson
We investigate decentralized gradient descent (DGD) with emphasis on efficient communication and computation resource utilization under budget constraints. As a first step toward t…
Distributed Optimization with Streaming Data: A Temporal Weighting Perspective
Muhammad Faraz Ul Abrar, Nicolò Michelusi, Erik G. Larsson
Optimization theory is a widely used tool for intelligent decision-making. While classical optimization deals with fixed, time-invariant objective functions, many modern applicatio…
Decentralized Time-Varying Optimization for Streaming Data via Temporal Weighting
Muhammad Faraz Ul Abrar, Nicolò Michelusi, Erik G. Larsson
Classical optimization theory largely focuses on fixed objective functions, whereas many modern learning systems operate in dynamic environments where data arrive sequentially and…
Time-Varying Optimization for Streaming Data Via Temporal Weighting
Muhammad Faraz Ul Abrar, Nicolò Michelusi, Erik G. Larsson
Classical optimization theory deals with fixed, time-invariant objective functions. However, time-varying optimization has emerged as an important subject for decision-making in dy…
Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Framework
Erik G. Larsson, Nicolo Michelusi
The decentralized gradient descent (DGD) algorithm, and its sibling, diffusion, are workhorses in decentralized machine learning, distributed inference and estimation, and multi-ag…