Compressed Gradient Tracking Algorithm for Distributed Aggregative Optimization
This article is devoted to addressing the distributed aggregative optimization (DAO) problem via compressed gradient tracking algorithms, where the cost function of each agent relies on the aggregation of other agents' decisions as well as its own decision. To this end, a new kind of the distri...
Uloženo v:
| Vydáno v: | IEEE transactions on automatic control Ročník 69; číslo 10; s. 6576 - 6591 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
01.10.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 0018-9286, 1558-2523 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | This article is devoted to addressing the distributed aggregative optimization (DAO) problem via compressed gradient tracking algorithms, where the cost function of each agent relies on the aggregation of other agents' decisions as well as its own decision. To this end, a new kind of the distributed aggregative gradient tracking algorithm with compression communication is developed based on the gradient tracking algorithm and the technique of communication compression. Under the scenario with a time-invariant and balanced graph, it is theoretically shown that the present algorithm owns a linear convergence rate (in the mean-square error sense) with the strongly convex and smooth cost functions. Furthermore, the result is extended to a more general case with the time-varying graphs considered. Specifically, it is proven that the developed algorithm could converge linearly to the optimal solution of the DAO problem (in the mean-square error sense) if the time-varying balanced graph is jointly strongly connected and some suitable conditions are satisfied. With the optimal placement problem considered, some numerical simulation results are performed to validate the theoretical results. |
|---|---|
| AbstractList | This article is devoted to addressing the distributed aggregative optimization (DAO) problem via compressed gradient tracking algorithms, where the cost function of each agent relies on the aggregation of other agents' decisions as well as its own decision. To this end, a new kind of the distributed aggregative gradient tracking algorithm with compression communication is developed based on the gradient tracking algorithm and the technique of communication compression. Under the scenario with a time-invariant and balanced graph, it is theoretically shown that the present algorithm owns a linear convergence rate (in the mean-square error sense) with the strongly convex and smooth cost functions. Furthermore, the result is extended to a more general case with the time-varying graphs considered. Specifically, it is proven that the developed algorithm could converge linearly to the optimal solution of the DAO problem (in the mean-square error sense) if the time-varying balanced graph is jointly strongly connected and some suitable conditions are satisfied. With the optimal placement problem considered, some numerical simulation results are performed to validate the theoretical results. |
| Author | Cao, Jinde Wen, Guanghui Yu, Wenwu Chen, Liyuan Liu, Hongzhe |
| Author_xml | – sequence: 1 givenname: Liyuan orcidid: 0009-0007-4171-0665 surname: Chen fullname: Chen, Liyuan email: clymath@seu.edu.cn organization: Department of Systems Science, School of Mathematics, Southeast University, Nanjing, China – sequence: 2 givenname: Guanghui orcidid: 0000-0003-0070-8597 surname: Wen fullname: Wen, Guanghui email: ghwen@seu.edu.cn organization: Department of Systems Science, School of Mathematics, Southeast University, Nanjing, China – sequence: 3 givenname: Hongzhe orcidid: 0000-0002-6887-368X surname: Liu fullname: Liu, Hongzhe email: 101300130@seu.edu.cn organization: Department of Systems Science, School of Mathematics, Southeast University, Nanjing, China – sequence: 4 givenname: Wenwu orcidid: 0000-0003-0301-9180 surname: Yu fullname: Yu, Wenwu email: wwyu@seu.edu.cn organization: Department of Systems Science, School of Mathematics, Southeast University, Nanjing, China – sequence: 5 givenname: Jinde orcidid: 0000-0003-3133-7119 surname: Cao fullname: Cao, Jinde email: jdcao@seu.edu.cn organization: School of Mathematics, Southeast University, Nanjing, China |
| BookMark | eNp9kDtPwzAURi1UJNrCzsAQiTnFr8TxGAUoSJW6lNlybCe4NA9sFwl-PS7tgBiYrj7pO_denRmY9ENvALhGcIEQ5HebslpgiOmCEIYKlp-BKcqyIsUZJhMwhRAVKcdFfgFm3m9jzClFU7Cuhm50xnujk6WT2po-JBsn1Zvt26TctYOz4bVLmsEl99YHZ-t9iN2ybZ1pZbAfJlmPwXb2K4ahvwTnjdx5c3Wac_Dy-LCpntLVevlclatUYY5DinKoOa0NMcxwIiFTUhWcE8JrRLCWWVGzGiIGc10QrThEjZE5IlpLojJGyRzcHveObnjfGx_Edti7Pp4UJPqgjGWUx1Z-bCk3eO9MI5QNP38GJ-1OICgO8kSUJw7yxEleBOEfcHS2k-7zP-TmiFhjzK86zQgnlHwDggB8Xg |
| CODEN | IETAA9 |
| CitedBy_id | crossref_primary_10_1016_j_neunet_2024_107085 crossref_primary_10_1016_j_sysconle_2025_106217 crossref_primary_10_1109_TSIPN_2025_3603740 crossref_primary_10_1016_j_neucom_2024_129022 |
| Cites_doi | 10.1109/tac.2024.3368967 10.1109/tnse.2023.3253143 10.1109/CDC49753.2023.10384194 10.1109/MSP.2020.2975210 10.1109/cdc51059.2022.9993052 10.1109/tac.2021.3095456 10.1109/TAC.2016.2626578 10.1109/TCNS.2014.2357513 10.1109/TCNS.2017.2698261 10.1016/j.ifacol.2022.07.227 10.1142/p821 10.1109/TSP.2019.2926022 10.1137/14096668X 10.1109/TAC.2022.3154356 10.1109/tac.2023.3241771 10.14736/kyb-2022-1-0123 10.1109/TAC.2020.3014095 10.1109/tac.2022.3145576 10.1002/rnc.6640 10.1109/tac.2022.3219289 10.1007/978-1-4419-8853-9 10.1137/16M1084316 10.1109/TAC.2020.2989281 10.1017/CBO9781139020411 10.1109/CDC45484.2021.9683433 10.1109/tac.2022.3180695 10.1109/TAC.2008.2009515 10.1109/TCNS.2021.3107480 10.1109/tac.2022.3225515 10.1109/TSP.2020.3031073 10.1109/TCNS.2018.2834310 10.1109/TSP.2022.3160238 10.1109/TAC.2022.3196627 10.1109/TAC.2020.2969721 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 7TB 8FD FR3 JQ2 L7M L~C L~D |
| DOI | 10.1109/TAC.2024.3371876 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1558-2523 |
| EndPage | 6591 |
| ExternalDocumentID | 10_1109_TAC_2024_3371876 10453934 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Key Research and Development Program of China grantid: 2022YFA1004702 – fundername: Jiangsu Provincial Scientific Research Center of Applied Mathematics grantid: BK20233002 – fundername: National Natural Science Foundation of China grantid: 62325304; U22B2046; 62073079; 61833005; 62088101; 62233004; 62203110; 62073076 funderid: 10.13039/501100001809 |
| GroupedDBID | -~X .DC 0R~ 29I 3EH 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IAAWW IBMZZ ICLAB IDIHD IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P RIA RIE RNS TAE TN5 VH1 VJK ~02 AAYXX CITATION 7SC 7SP 7TB 8FD FR3 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c292t-160d94be3e7e93a07cac899339b132da58b7b01706d83dc901fea613dda3c5743 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 4 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001322635200012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0018-9286 |
| IngestDate | Mon Jun 30 10:07:11 EDT 2025 Sat Nov 29 05:41:11 EST 2025 Tue Nov 18 22:24:33 EST 2025 Wed Aug 27 02:02:24 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c292t-160d94be3e7e93a07cac899339b132da58b7b01706d83dc901fea613dda3c5743 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-6887-368X 0000-0003-0301-9180 0009-0007-4171-0665 0000-0003-0070-8597 0000-0003-3133-7119 |
| PQID | 3110477549 |
| PQPubID | 85475 |
| PageCount | 16 |
| ParticipantIDs | crossref_citationtrail_10_1109_TAC_2024_3371876 crossref_primary_10_1109_TAC_2024_3371876 ieee_primary_10453934 proquest_journals_3110477549 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-10-01 |
| PublicationDateYYYYMMDD | 2024-10-01 |
| PublicationDate_xml | – month: 10 year: 2024 text: 2024-10-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on automatic control |
| PublicationTitleAbbrev | TAC |
| PublicationYear | 2024 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref35 ref12 ref34 ref15 ref37 ref14 ref36 Koloskova (ref10) 2019 Alistarh (ref11) 2017 ref33 ref2 ref1 Guido (ref30) 2022 ref17 ref39 ref16 ref38 ref19 Liu (ref32) 2023 ref18 ref24 ref23 Liu (ref13) 2020 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 Du (ref31) 2023 ref6 ref5 ref40 |
| References_xml | – ident: ref24 doi: 10.1109/tac.2024.3368967 – ident: ref33 doi: 10.1109/tnse.2023.3253143 – ident: ref25 doi: 10.1109/CDC49753.2023.10384194 – start-page: 1576 volume-title: Proc. IEEE 61st Conf. Decis. Control year: 2022 ident: ref30 article-title: A learning-based distributed algorithm for personalized aggregative optimization – ident: ref3 doi: 10.1109/MSP.2020.2975210 – ident: ref34 doi: 10.1109/cdc51059.2022.9993052 – ident: ref22 doi: 10.1109/tac.2021.3095456 – ident: ref2 doi: 10.1109/TAC.2016.2626578 – ident: ref16 doi: 10.1109/TCNS.2014.2357513 – ident: ref7 doi: 10.1109/TCNS.2017.2698261 – ident: ref23 doi: 10.1016/j.ifacol.2022.07.227 – ident: ref36 doi: 10.1142/p821 – ident: ref4 doi: 10.1109/TSP.2019.2926022 – ident: ref5 doi: 10.1137/14096668X – ident: ref26 doi: 10.1109/TAC.2022.3154356 – ident: ref8 doi: 10.1109/tac.2023.3241771 – start-page: 3478 volume-title: Proc. 36th Int. Conf. Mach. Learn. year: 2019 ident: ref10 article-title: Decentralized stochastic optimization and gossip algorithms with compressed communication – volume-title: Proc. Int. Conf. Learn. Representations year: 2020 ident: ref13 article-title: Linear convergent decentralized optimization with compression – ident: ref35 doi: 10.14736/kyb-2022-1-0123 – ident: ref17 doi: 10.1109/TAC.2020.3014095 – ident: ref12 doi: 10.1109/tac.2022.3145576 – year: 2023 ident: ref31 article-title: Linear convergence of distributed aggregative optimization with coupled inequality constraints – ident: ref29 doi: 10.1002/rnc.6640 – year: 2023 ident: ref32 article-title: Accelerated distributed aggregative optimization – ident: ref20 doi: 10.1109/tac.2022.3219289 – ident: ref37 doi: 10.1007/978-1-4419-8853-9 – ident: ref6 doi: 10.1137/16M1084316 – ident: ref19 doi: 10.1109/TAC.2020.2989281 – ident: ref38 doi: 10.1017/CBO9781139020411 – ident: ref21 doi: 10.1109/CDC45484.2021.9683433 – ident: ref14 doi: 10.1109/tac.2022.3180695 – ident: ref40 doi: 10.1109/TAC.2008.2009515 – ident: ref28 doi: 10.1109/TCNS.2021.3107480 – ident: ref9 doi: 10.1109/tac.2022.3225515 – ident: ref18 doi: 10.1109/TSP.2020.3031073 – ident: ref1 doi: 10.1109/TCNS.2018.2834310 – ident: ref15 doi: 10.1109/TSP.2022.3160238 – start-page: 1709 volume-title: Proc. Int. Conf. Adv. Neural Inf. Process. Syst. year: 2017 ident: ref11 article-title: QSGD: Communication-efficient SGD via gradient quantization and encoding – ident: ref27 doi: 10.1109/TAC.2022.3196627 – ident: ref39 doi: 10.1109/TAC.2020.2969721 |
| SSID | ssj0016441 |
| Score | 2.4831927 |
| Snippet | This article is devoted to addressing the distributed aggregative optimization (DAO) problem via compressed gradient tracking algorithms, where the cost... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 6576 |
| SubjectTerms | Algorithms Communication compression Compressors Convergence Cost function distributed aggregative optimization (DAO) Gradient methods gradient tracking algorithm Linear matrix inequalities Machine learning algorithms Optimization Prediction algorithms Time-varying channels time-varying graph Tracking |
| Title | Compressed Gradient Tracking Algorithm for Distributed Aggregative Optimization |
| URI | https://ieeexplore.ieee.org/document/10453934 https://www.proquest.com/docview/3110477549 |
| Volume | 69 |
| WOSCitedRecordID | wos001322635200012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE/IET Electronic Library customDbUrl: eissn: 1558-2523 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016441 issn: 0018-9286 databaseCode: RIE dateStart: 19630101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELZoxQADzyIKBWVgYUib2Ikdj1GhMKCWoUjdIr9SKtEWpSm_n7OTVpUQSGwZ7Ci6yz0-n-87hO6wTGLKsfCZwNSPiIx9LjhgHhYoTLXIpXRTS17YcJhMJvy1blZ3vTDGGHf5zHTto6vl66Va26MysPAoJpxEDdRgjFbNWtuSgQ3sldsFC8bJtiYZ8N447QMSxFGXEHDFll5kJwa5oSo_PLELL4Pjf37YCTqq80gvrRR_ivbM4gwd7rALnqORtXXHDa69p8Jd7So9iE3Kno576cd0WczK97kHaav3YPlz7egrWJtOAYNPHSG4NwKPMq9bNVvobfA47j_79fwEX2GOSz-kgeaRNMQww4kImBIK4BUhXAIG1SJOJJOOP0cnRCvIDHIjILxrLYiKIbW4QM3FcmEukZfQKKdUshznONKKJbGAWM9CLRUTYWjaqLeRaKZqcnE74-IjcyAj4BnoILM6yGodtNH9dsdnRazxx9qWlfnOukrcbdTZaC2rTW-VkdCyTzDAvVe_bLtGB_bt1ZW8DmqWxdrcoH31Vc5Wxa37q74BEoDJkA |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3JTsMwEB2xScCBtYiy5sCFQ9rETuL4WBUKiFI4FIlb5C2lUhdUAt_P2AlVJQQStxxsJZrJLM_jeQNwQWQaJ5wInwmS-BGVsc8FR8zDAkUSLXIp3dSSLuv10pcX_lQ1q7teGGOMu3xmGvbR1fL1VH3YozK08CimnEbLsGpHZ1XtWvOigQ3tpeNFGybpvCoZ8Ga_1UYsSKIGpeiMLcHIQhRyY1V--GIXYDrb__y0HdiqMkmvVap-F5bMZA82F_gF9-HRWrtjB9fezcxd7io8jE7Kno97rdFgOhsWr2MPE1fvyjLo2uFXuLY1QBQ-cJTg3iP6lHHVrFmD5851v33rVxMUfEU4KfwwCTSPpKGGGU5FwJRQCLAo5RJRqBZxKpl0DDo6pVphbpAbgQFea0FVjMnFAaxMphNzCF6aRHmSSJaTnERasTQWGO1ZqKViIgxNHZrfEs1URS9up1yMMgczAp6hDjKrg6zSQR0u5zveSmqNP9bWrMwX1pXirsPJt9ayyvjeMxpa_gmGyPfol23nsH7bf-hm3bve_TFs2DeVF_ROYKWYfZhTWFOfxfB9dub-sC--RczZ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Compressed+Gradient+Tracking+Algorithm+for+Distributed+Aggregative+Optimization&rft.jtitle=IEEE+transactions+on+automatic+control&rft.au=Chen%2C+Liyuan&rft.au=Wen%2C+Guanghui&rft.au=Liu%2C+Hongzhe&rft.au=Yu%2C+Wenwu&rft.date=2024-10-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=0018-9286&rft.eissn=1558-2523&rft.volume=69&rft.issue=10&rft.spage=6576&rft_id=info:doi/10.1109%2FTAC.2024.3371876&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9286&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9286&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9286&client=summon |