Distributed ℓ0 Sparse Aggregative Optimization.

Alireza Olama*, Guido Carnevale, Giuseppe Notarstefano, Eduardo Camponogara

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

Abstract

Sparse convex optimization involves optimization problems where the decision variables are constrained to have a certain number of entries equal to zero. In this paper, we focus on the sparse version of the so-called aggregative optimization scenario, i.e., on optimization problems in which the cost reads as the sum of local functions each depending on both a local decision variable and an aggregation of all of them. In this framework, we propose a novel fully-distributed scheme to address the problem over a network of cooperating agents. Specifically, by taking advantage of a suitable problem reformulation, we define an Augmented Lagrangian function. Then, we address such an Augmented Lagrangian by suitably interlacing the so-called Projected Aggregative Tracking distributed algorithm and the Block Coordinated Descent method giving rise to a novel fully-distributed scheme. The effectiveness of the proposed algorithm is corroborated via numerical simulations in problems arising in machine learning scenarios with both synthetic and real-world data sets.

Original languageEnglish
Title of host publication2024 IEEE 20th International Conference on Automation Science and Engineering, CASE 2024
PublisherIEEE
Pages1747-1752
Number of pages6
ISBN (Electronic)9798350358513
ISBN (Print)979-8-3503-5852-0
DOIs
Publication statusPublished - 23 Oct 2024
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Automation Science and Engineering -
Duration: 28 Aug 2024 → …

Publication series

NameIEEE International Conference on Automation Science and Engineering
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

ConferenceIEEE International Conference on Automation Science and Engineering
Abbreviated titleCASE
Period28/08/24 → …

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