75 citations · 142 across the 21 of their papers we have counts for
24 papers
Delay-agnostic Asynchronous Coordinate Update Algorithm
Xuyang Wu, Changxin Liu, Sindri Magnusson +1
We propose a delay-agnostic asynchronous coordinate update algorithm (DEGAS) for computing operator fixed points, with applications to asynchronous optimization. DEGAS includes nov…
Comparing NARS and Reinforcement Learning: An Analysis of ONA and -Learning Algorithms
Ali Beikmohammadi, Sindri Magnússon
In recent years, reinforcement learning (RL) has emerged as a popular approach for solving sequence-based tasks in machine learning. However, finding suitable alternatives to RL re…
Delay-agnostic Asynchronous Distributed Optimization
Xuyang Wu, Changxin Liu, Sindri Magnusson +1
Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or fixed step-sizes that depend on an assumed uppe…
Human-Inspired Framework to Accelerate Reinforcement Learning
Ali Beikmohammadi, Sindri Magnússon
Reinforcement learning (RL) is crucial for data science decision-making but suffers from sample inefficiency, particularly in real-world scenarios with costly physical interactions…
Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities
Nancy Victor, Rajeswari. C, Mamoun Alazab +5
Internet of Underwater Things (IoUT) have gained rapid momentum over the past decade with applications spanning from environmental monitoring and exploration, defence applications,…
Distributed Safe Resource Allocation using Barrier Functions
Xuyang Wu, Sindri Magnusson, Mikael Johansson
Resource allocation plays a central role in many networked systems such as smart grids, communication networks and urban transportation systems. In these systems, many constraints…
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…
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…
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…
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…
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…
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…
Communication-efficient Variance-reduced Stochastic Gradient Descent
Hossein S. Ghadikolaei, Sindri Magnusson
We consider the problem of communication efficient distributed optimization where multiple nodes exchange important algorithm information in every iteration to solve large problems…
A flexible framework for communication-efficient machine learning: from HPC to IoT
Sarit Khirirat, Sindri Magnússon, Arda Aytekin +1
With the increasing scale of machine learning tasks, it has become essential to reduce the communication between computing nodes. Early work on gradient compression focused on the…
Compressed Gradient Methods with Hessian-Aided Error Compensation
Sarit Khirirat, Sindri Magnússon, Mikael Johansson
The emergence of big data has caused a dramatic shift in the operating regime for optimization algorithms. The performance bottleneck, which used to be computations, is now often c…
Distributed Optimal Voltage Control with Asynchronous and Delayed Communication
Sindri Magnússon, Guannan Qu, Na Li
The increased penetration of volatile renewable energy into distribution networks necessities more efficient distributed voltage control. In this paper, we design distributed feedb…
On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication
Sindri Magnússon, Hossein Shokri-Ghadikolaei, Na Li
In distributed optimization and machine learning, multiple nodes coordinate to solve large problems. To do this, the nodes need to compress important algorithm information to bits…
Voltage Control Using Limited Communication
Sindri Magnusson, Guannan Qu, Carlo Fischione +1
In electricity distribution networks, the increasing penetration of renewable energy generation necessitates faster and more sophisticated voltage controls. Unfortunately, recent r…
Convergence of Limited Communications Gradient Methods
Sindri Magnusson, Chinwendu Enyioha, Na Li +2
Distributed optimization increasingly plays a central role in economical and sustainable operation of cyber-physical systems. Nevertheless, the complete potential of the technology…
Robustness Analysis for an Online Decentralized Descent Power allocation algorithm
Chinwendu Enyioha, Sindri Magnusson, Kathryn Heal +3
As independent service providers shift from conventional energy to renewable energy sources, the power distribution system will likely experience increasingly significant fluctuati…
Distributed Resource Allocation Using One-Way Communication with Applications to Power Networks
Sindri Magnusson, Chinwendu Enyioha, Kathryn Heal +3
Typical coordination schemes for future power grids require two-way communications. Since the number of end power-consuming devices is large, the bandwidth requirements for such tw…
On the Convergence of Alternating Direction Lagrangian Methods for Nonconvex Structured Optimization Problems
Sindri Magnússon, Pradeep Chathuranga Weeraddana, Michael G. Rabbat +1
Nonconvex and structured optimization problems arise in many engineering applications that demand scalable and distributed solution methods. The study of the convergence properties…
A Distributed Approach for the Optimal Power Flow Problem Based on ADMM and Sequential Convex Approximations
S. Magnússon, P. C. Weeraddana, C. Fischione
The optimal power flow (OPF) problem, which plays a central role in operating electrical networks is considered. The problem is nonconvex and is in fact NP hard. Therefore, designi…
Extensions of Fast-Lipschitz Optimization
Martin Jakobsson, Carlo Fischione, Pradeep Chathuranga Weeraddana
The need of fast distributed solvers for optimization problems in networked systems has motivated the recent development of the Fast-Lipschitz optimization framework. In such an op…