activity
20192022
most citedOn the Convergence of Step Decay Step-Size for Stochastic Optimization

4 citations · 7 across the 5 of their papers we have counts for

collaborators

10 papers

eess.SY20222 cited

Leakage Localization in Water Distribution Networks: A Model-Based Approach

Ludvig Lindstrom, Sebin Gracy, Sindri Magnusson +1

The paper studies the problem of leakage localization in water distribution networks. For the case of a single pipe that suffers from a single leak, by taking recourse to pressure…

math.OC2022

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…

cs.LG20221 cited

Delay-adaptive step-sizes for asynchronous learning

Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian +1

In scalable machine learning systems, model training is often parallelized over multiple nodes that run without tight synchronization. Most analysis results for the related asynchr…

math.OC2021

A New Family of Feasible Methods for Distributed Resource Allocation

Xuyang Wu, Sindri Magnusson, Mikael Johansson

Distributed resource allocation is a central task in network systems such as smart grids, water distribution networks, and urban transportation systems. When solving such problems…

math.OC20214 cited

On the Convergence of Step Decay Step-Size for Stochastic Optimization

Xiaoyu Wang, Sindri Magnússon, Mikael Johansson

The convergence of stochastic gradient descent is highly dependent on the step-size, especially on non-convex problems such as neural network training. Step decay step-size schedul…

cs.LG2020

The Internet of Things as a Deep Neural Network

Rong Du, Sindri Magnússon, Carlo Fischione

An important task in the Internet of Things (IoT) is field monitoring, where multiple IoT nodes take measurements and communicate them to the base station or the cloud for processi…