Multi-agent distributed optimization algorithms for partition-based linear programming (LP) problems

Ruggero Carli, Kasim Sinan Yildirim, Luca Schenato

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


The paper addresses the problem of multi-agent distributed solutions for a class of linear programming (LP) problems which include box constraints on the decision variables and inequality constraints. The major difference with existing literature on distributed solution of LP problems is that each agent is expected to compute only a single or few entries of the global minimizer vector, often referred as a partition-based optimization. This class of LP problems isrelevant in different applications such as optimal power transfer in remotely powered battery-less wireless sensor networks, minimum energy LED luminaries control in smart offices, and optimal temperature control in start buildings. Via a suitable approximation of the originalLP problem, we propose three different primal-dual distributed algorithms based on dual gradient ascent, on the methods of multipliers and on the Alternating Direction Methods of Multipliers.We discuss the computational and communication requirements of these methods and we provide numerical comparisons.

Original languageEnglish
Title of host publication2018 European Control Conference, ECC 2018
EditorsMarios Polycarpou, Thomas Parisini, Christos Panayiotou, Christoforos Hadjicostis
Place of PublicationPiscataway, NJ, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)978-3-9524-2698-2
ISBN (Print)978-3-9524-2699-9
Publication statusPublished - 2018
Event16th European Control Conference, ECC 2018 - Limassol, Cyprus
Duration: 12 Jun 201815 Jun 2018


Conference16th European Control Conference, ECC 2018
Abbreviated titleECC 2018
Internet address


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