Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Convergence of Limited Communication Gradient Methods
KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA..
Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA..
KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.ORCID iD: 0000-0001-9810-3478
Show others and affiliations
2018 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 63, no 5, p. 1356-1371Article in journal (Refereed) Published
Abstract [en]

Distributed optimization increasingly plays a central role in economical and sustainable operation of cyber-physical systems. Nevertheless, the complete potential of the technology has not yet been fully exploited in practice due to communication limitations posed by the real-world infrastructures. This work investigates fundamental properties of distributed optimization based on gradient methods, where gradient information is communicated using a limited number of bits. In particular, a general class of quantized gradient methods are studied, where the gradient direction is approximated by a finite quantization set. Sufficient and necessary conditions are provided on such a quantization set to guarantee that the methods minimize any convex objective function with Lipschitz continuous gradient and a nonempty and bounded set of optimizers. A lower bound on the cardinality of the quantization set is provided, along with specific examples of minimal quantizations. Convergence rate results are established that connect the fineness of the quantization and the number of iterations needed to reach a predefined solution accuracy. Generalizations of the results to a relevant class of constrained problems using projections are considered. Finally, the results are illustrated by simulations of practical systems.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 63, no 5, p. 1356-1371
Keywords [en]
Cyberphysical systems, distributed optimization, limited communication
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-227744DOI: 10.1109/TAC.2017.2743678ISI: 000430968300010Scopus ID: 2-s2.0-85028517757OAI: oai:DiVA.org:kth-227744DiVA, id: diva2:1205737
Note

QC 20180515

Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-05-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Fischione, Carlo

Search in DiVA

By author/editor
Magnusson, SindriFischione, Carlo
By organisation
Network and Systems engineering
In the same journal
IEEE Transactions on Automatic Control
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 6 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf