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A Semismooth Newton Method for Adaptive Distributed Sparse Linear Regression

Shutin, Dmitriy and Vexler, Boris (2015) A Semismooth Newton Method for Adaptive Distributed Sparse Linear Regression. In: 2015 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2015-12-13 - 2015-12-16, Cancun, Mexico. doi: 10.1109/CAMSAP.2015.7383829. ISBN 978-1-4799-1963-5.

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Abstract

The presented work studies an application of a technique known as a semismooth Newton (SSN) method to accelerate the convergence of distributed quadratic programming LASSO (DQP-LASSO) - a consensus-based distributed sparse linear regression algorithm. The DQP-LASSO algorithm exploits an alternating directions method of multipliers (ADMM) algorithm to reduce a global LASSO problem to a series of local (per agent) LASSO optimizations, which outcomes are then appropriately combined. The SSN algorithm enjoys superlinear convergence and thus permits implementing these local optimizations more efficiently. Yet in some cases SSN might experience convergence issues. Here it is shown that the ADMM-inherent regularization also provides sufficient regularization to stabilize the SSN algorithm, thus ensuring a stable convergence of the whole scheme. Additionally, the structure of the SSN algorithm also permits an adaptive implementation of a distributed sparse regression. This allows for an estimation of time-varying sparse vectors, as well as leverages storage requirements for processing streams of data.

Item URL in elib:https://elib.dlr.de/98395/
Document Type:Conference or Workshop Item (Poster)
Title:A Semismooth Newton Method for Adaptive Distributed Sparse Linear Regression
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Shutin, DmitriyUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Vexler, BorisUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:13 December 2015
Journal or Publication Title:2015 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI:10.1109/CAMSAP.2015.7383829
ISBN:978-1-4799-1963-5
Status:Published
Keywords:Distributed sparse regression, multi-agent systems, smart networks, Semismooth Newton methods
Event Title:IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Event Location:Cancun, Mexico
Event Type:international Conference
Event Start Date:13 December 2015
Event End Date:16 December 2015
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Communication and Navigation
DLR - Research area:Raumfahrt
DLR - Program:R KN - Kommunikation und Navigation
DLR - Research theme (Project):R - Project Dependable Navigation (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation > Communications Systems
Deposited By: Shutin, Dmitriy
Deposited On:10 Feb 2016 15:52
Last Modified:24 Apr 2024 20:03

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