Concept

Topologies on spaces of linear maps

Related publications (48)

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This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents. This scenario arises in many machine learning and engineering ...

IEEE2020

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We propose a novel sparse dictionary learning method for planar shapes in the sense of Kendall, namely configurations of landmarks in the plane considered up to similitudes. Our shape dictionary method provides a good trade-off between algorithmic simplici ...

SIAM PUBLICATIONS2020

We study asymmetric zero-range processes on Z with nearest-neighbour jumps and site disorder. The jump rate of particles is an arbitrary but bounded nondecreasing function of the number of particles. We prove quenched strong local equilibrium at subcritica ...

SPRINGER HEIDELBERG2020

Decentralized optimization methods enable on-device training of machine learning models without a central coordinator. In many scenarios communication between devices is energy demanding and time consuming and forms the bottleneck of the entire system. We ...

2020

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Total flux expansion, a divertor magnetic topology design choice embodied in the Super-X divertor, is predicted through simple analytic models and SOLPS calculations to reduce the plasma and impurity density detachment thresholds as the outer divertor sepa ...

IOP PUBLISHING LTD2020

Advances in controlled radical polymerization over the past two decades have transformed the ability to produce molecularly well-defined, chain-end tethered polymer brush films. The assets of controlled radical polymerization reactions have impacted the sy ...

PERGAMON-ELSEVIER SCIENCE LTD2020

On the convergence of stochastic primal-dual hybrid gradient

Volkan Cevher, Ahmet Alacaoglu

In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algorithm and provide new theoretical results. In particular, we prove almost sure convergence of the iterates to a solution and linear convergence with standard ...

2019

A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

Decentralized optimization is a powerful paradigm that finds applications in engineering and learning design. This work studies decentralized composite optimization problems with non-smooth regularization terms. Most existing gradient-based proximal decent ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Variance-Reduced Stochastic Learning by Networked Agents Under Random Reshuffling

Ali H. Sayed, Bicheng Ying, Kun Yuan

This paper develops a distributed variance-reduced strategy for a collection of interacting agents that are connected by a graph topology. The resulting diffusion-AVRG (where AVRG stands for "amortized variance-reduced gradient") algorithm is shown to have ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

On Cyclic Dependencies and Regulators in Time-Sensitive Networks

Jean-Yves Le Boudec, Ludovic Bernard Gérard Thomas

For time-sensitive networks, as in the context of IEEE TSN and IETF Detnet, cyclic dependencies are associated with certain fundamental properties such as improving availability and decreasing reconfiguration effort. Nevertheless, the existence of cyclic d ...

IEEE2019