A conditional gradient algorithm for distributed online optimization in networks
This paper addresses a network of computing nodes aiming to solve an online convex optimisation problem in a distributed manner, that is, by means of the local estimation and communication, without any central coordinator. An online distributed conditional gradient algorithm based on the conditional...
Uloženo v:
| Vydáno v: | IET control theory & applications Ročník 15; číslo 4; s. 570 - 579 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Wiley
01.03.2021
|
| Témata: | |
| ISSN: | 1751-8644, 1751-8652 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | This paper addresses a network of computing nodes aiming to solve an online convex optimisation problem in a distributed manner, that is, by means of the local estimation and communication, without any central coordinator. An online distributed conditional gradient algorithm based on the conditional gradient is developed, which can effectively tackle the problem of high time complexity of the distributed online optimisation. The proposed algorithm allows the global objective function to be decomposed into the sum of the local objective functions, and nodes collectively minimise the sum of local time‐varying objective functions while the communication pattern among nodes is captured as a connected undirected graph. By adding a regularisation term to the local objective function of each node, the proposed algorithm constructs a new time‐varying objective function. The proposed algorithm also utilises the local linear optimisation oracle to replace the projection operation such that the regret bound of the algorithm can be effectively improved. By introducing the nominal regret and the global regret, the convergence properties of the proposed algorithm are also theoretically analysed. It is shown that, if the objective function of each agent is strongly convex and smooth, these two types of regrets grow sublinearly with the order of O(logT), where T is the time horizon. Numerical experiments also demonstrate the advantages of the proposed algorithm over existing distributed optimisation algorithms. |
|---|---|
| AbstractList | This paper addresses a network of computing nodes aiming to solve an online convex optimisation problem in a distributed manner, that is, by means of the local estimation and communication, without any central coordinator. An online distributed conditional gradient algorithm based on the conditional gradient is developed, which can effectively tackle the problem of high time complexity of the distributed online optimisation. The proposed algorithm allows the global objective function to be decomposed into the sum of the local objective functions, and nodes collectively minimise the sum of local time‐varying objective functions while the communication pattern among nodes is captured as a connected undirected graph. By adding a regularisation term to the local objective function of each node, the proposed algorithm constructs a new time‐varying objective function. The proposed algorithm also utilises the local linear optimisation oracle to replace the projection operation such that the regret bound of the algorithm can be effectively improved. By introducing the nominal regret and the global regret, the convergence properties of the proposed algorithm are also theoretically analysed. It is shown that, if the objective function of each agent is strongly convex and smooth, these two types of regrets grow sublinearly with the order of , where is the time horizon. Numerical experiments also demonstrate the advantages of the proposed algorithm over existing distributed optimisation algorithms. This paper addresses a network of computing nodes aiming to solve an online convex optimisation problem in a distributed manner, that is, by means of the local estimation and communication, without any central coordinator. An online distributed conditional gradient algorithm based on the conditional gradient is developed, which can effectively tackle the problem of high time complexity of the distributed online optimisation. The proposed algorithm allows the global objective function to be decomposed into the sum of the local objective functions, and nodes collectively minimise the sum of local time‐varying objective functions while the communication pattern among nodes is captured as a connected undirected graph. By adding a regularisation term to the local objective function of each node, the proposed algorithm constructs a new time‐varying objective function. The proposed algorithm also utilises the local linear optimisation oracle to replace the projection operation such that the regret bound of the algorithm can be effectively improved. By introducing the nominal regret and the global regret, the convergence properties of the proposed algorithm are also theoretically analysed. It is shown that, if the objective function of each agent is strongly convex and smooth, these two types of regrets grow sublinearly with the order of O(logT), where T is the time horizon. Numerical experiments also demonstrate the advantages of the proposed algorithm over existing distributed optimisation algorithms. Abstract This paper addresses a network of computing nodes aiming to solve an online convex optimisation problem in a distributed manner, that is, by means of the local estimation and communication, without any central coordinator. An online distributed conditional gradient algorithm based on the conditional gradient is developed, which can effectively tackle the problem of high time complexity of the distributed online optimisation. The proposed algorithm allows the global objective function to be decomposed into the sum of the local objective functions, and nodes collectively minimise the sum of local time‐varying objective functions while the communication pattern among nodes is captured as a connected undirected graph. By adding a regularisation term to the local objective function of each node, the proposed algorithm constructs a new time‐varying objective function. The proposed algorithm also utilises the local linear optimisation oracle to replace the projection operation such that the regret bound of the algorithm can be effectively improved. By introducing the nominal regret and the global regret, the convergence properties of the proposed algorithm are also theoretically analysed. It is shown that, if the objective function of each agent is strongly convex and smooth, these two types of regrets grow sublinearly with the order of O(logT), where T is the time horizon. Numerical experiments also demonstrate the advantages of the proposed algorithm over existing distributed optimisation algorithms. |
| Author | Dong, Qiao Fang, Runyue Li, Dequan Shen, Xiuyu |
| Author_xml | – sequence: 1 givenname: Xiuyu surname: Shen fullname: Shen, Xiuyu organization: Anhui University of Science and Technology – sequence: 2 givenname: Dequan surname: Li fullname: Li, Dequan email: leedqcpp@126.com organization: Anhui University of Science and Technology – sequence: 3 givenname: Runyue surname: Fang fullname: Fang, Runyue organization: Anhui University of Science and Technology – sequence: 4 givenname: Qiao surname: Dong fullname: Dong, Qiao organization: Anhui University of Science and Technology |
| BookMark | eNp9kEtLQzEQhYNUsFY3_oKshdY8b-5dlqK2IOiirkOaTGrqbVJyI0V_vX2oCxFXMwznO8w556gXUwSErigZUSKaG1te2IgyUrET1KdK0mFdSdb72YU4Q-ddtyJEykrIPnoaY5uiCyWkaFq8zMYFiAWbdplyKC9r7FPGLnQlh8VbAYdTbEMEnDYlrMOH2YM4RByhbFN-7S7QqTdtB5dfc4Ce727nk-nw4fF-Nhk_DC1Xkg0Z1KKmykFTG8Utp7ZWXpHGOE-UrawCzqSXDihpGlYzwhw3hksrATgIzgdodvR1yaz0Joe1ye86maAPh5SX2uQSbAuaSmmIJ4KrGoRii4WqpKCs8VVtTcX8zoscvWxOXZfBaxvKIVnJJrSaEr1vV-_b1Yd2d8j1L-T7hT_F9Cjehhbe_1HqyXzKjswneo-Mlw |
| CitedBy_id | crossref_primary_10_1002_rnc_6568 crossref_primary_10_1049_cth2_12421 |
| Cites_doi | 10.1109/TSP.2018.2830299 10.1109/TAC.2008.2009515 10.1137/080716542 10.1137/S0036144503423264 10.1109/TSP.2017.2771731 10.1109/TAC.2011.2161027 10.1049/iet-cta.2018.5585 10.1109/TNSE.2014.2363554 10.1137/18M119046X 10.1109/CDC.2013.6760092 10.1109/TAC.2014.2298712 10.1109/TKDE.2012.191 10.1007/s10957-010-9737-7 10.1049/iet-cta.2019.0020 10.1002/nav.3800030109 10.1109/TAC.2019.2937496 10.1109/TCNS.2015.2505149 10.1137/140985366 10.1038/30918 10.1137/1.9781611970791 10.1007/s10994-007-5016-8 10.1002/rnc.5199 10.1049/iet-cta.2017.0064 10.1109/TAC.2019.2930234 10.1109/TAC.2010.2041686 10.1016/j.automatica.2018.11.056 |
| ContentType | Journal Article |
| Copyright | 2020 The Authors. published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology |
| Copyright_xml | – notice: 2020 The Authors. published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology |
| DBID | 24P AAYXX CITATION DOA |
| DOI | 10.1049/cth2.12062 |
| DatabaseName | Wiley Online Library Open Access CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| Database_xml | – sequence: 1 dbid: 24P name: Wiley Online Library Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1751-8652 |
| EndPage | 579 |
| ExternalDocumentID | oai_doaj_org_article_155a0f04378e472bb7654129f68ca62f 10_1049_cth2_12062 CTH212062 |
| Genre | article |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China funderid: 61472003 |
| GroupedDBID | .DC 0R~ 0ZK 1OC 24P 29I 3V. 4.4 4IJ 5GY 6IK 8FE 8FG 8VB 96U AAHHS AAHJG AAJGR ABJCF ABQXS ABUWG ACCFJ ACCMX ACESK ACGFS ACIWK ACXQS ADEYR AEEZP AEGXH AENEX AEQDE AFAZI AFKRA AIWBW AJBDE ALMA_UNASSIGNED_HOLDINGS ALUQN ARAPS AVUZU AZQEC BENPR BGLVJ BPHCQ CCPQU CS3 DU5 DWQXO EBS EJD ESX F8P GNUQQ GOZPB GROUPED_DOAJ GRPMH HCIFZ HZ~ IAO IFIPE IPLJI ITC JAVBF K1G K6V K7- L6V LAI M0N M43 M7S MCNEO MS~ NADUK NXXTH O9- OCL OK1 P62 PQQKQ PROAC PTHSS QWB RIE RNS ROL RUI U5U UNMZH ZL0 ~ZZ AAMMB AAYXX AEFGJ AFFHD AGXDD AIDQK AIDYY CITATION IDLOA IGS PHGZM PHGZT PQGLB WIN |
| ID | FETCH-LOGICAL-c3752-2e84817de98a73c31c87f709adf07c6c7e325f5de109928202d3aa35c5ee3e433 |
| IEDL.DBID | 24P |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000602729400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1751-8644 |
| IngestDate | Fri Oct 03 12:42:33 EDT 2025 Wed Oct 29 21:29:30 EDT 2025 Tue Nov 18 21:06:23 EST 2025 Wed Jan 22 16:58:37 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| License | Attribution |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3752-2e84817de98a73c31c87f709adf07c6c7e325f5de109928202d3aa35c5ee3e433 |
| OpenAccessLink | https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fcth2.12062 |
| PageCount | 10 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_155a0f04378e472bb7654129f68ca62f crossref_citationtrail_10_1049_cth2_12062 crossref_primary_10_1049_cth2_12062 wiley_primary_10_1049_cth2_12062_CTH212062 |
| PublicationCentury | 2000 |
| PublicationDate | March 2021 2021-03-00 2021-03-01 |
| PublicationDateYYYYMMDD | 2021-03-01 |
| PublicationDate_xml | – month: 03 year: 2021 text: March 2021 |
| PublicationDecade | 2020 |
| PublicationTitle | IET control theory & applications |
| PublicationYear | 2021 |
| Publisher | Wiley |
| Publisher_xml | – name: Wiley |
| References | 2010; 55 2013; 25 2017; 4 2012 2019; 13 2017; 66 2010; 147 2019; 57 2004; 46 1996 2020; 14 2018; 66 2012; 57 1998; 393 2019; 101 2014; 1 2009; 54 1990 2020; 30 2017; 11 2014; 59 2005; 6 2017 1994; 13 2017; 18 2020; 65 2013 2009; 2 2016; 26 2016; 48 1956; 3 2007; 69 e_1_2_7_6_1 e_1_2_7_5_1 e_1_2_7_4_1 Horn R.A. (e_1_2_7_26_1) 1990 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_13_1 e_1_2_7_12_1 e_1_2_7_11_1 e_1_2_7_10_1 e_1_2_7_29_1 Colin I. (e_1_2_7_31_1) 2016; 48 Xu H. (e_1_2_7_14_1) 2005; 6 Hiriart‐Urruty J. B. (e_1_2_7_28_1) 1996 e_1_2_7_30_1 e_1_2_7_25_1 e_1_2_7_24_1 e_1_2_7_32_1 e_1_2_7_23_1 e_1_2_7_33_1 e_1_2_7_34_1 e_1_2_7_21_1 e_1_2_7_20_1 Kasai H. (e_1_2_7_27_1) 2017; 18 Zhang W. (e_1_2_7_16_1) 2017 Hazan E. (e_1_2_7_22_1) 2012 |
| References_xml | – volume: 147 start-page: 516 year: 2010 end-page: 545 article-title: Distributed stochastic subgradient projection algorithms for convex optimization publication-title: J. Optim. Theory Appl. – volume: 6 start-page: 1595 year: 2005 end-page: 1599 article-title: Decentralized online alternating direction method of multipliers publication-title: J. Comput. Appl. – start-page: 484 year: 2013 end-page: 1489 – start-page: 521 year: 2012 end-page: 528 – start-page: 4054 year: 2017 end-page: 4062 – volume: 4 start-page: 417 year: 2017 end-page: 428 article-title: Distributed online convex optimization on time‐varying directed graphs publication-title: IEEE Trans. Control Netw. Syst. – volume: 30 start-page: 7574 year: 2020 end-page: 7592 article-title: Distributed proximal‐gradient algorithms for nonsmooth convex optimization of second‐order multiagent systems publication-title: Int. J. Robust Nonlinear Control – volume: 54 start-page: 48 year: 2009 end-page: 61 article-title: Distributed subgradient methods for multi‐agent optimization publication-title: IEEE Trans. Autom. Control – volume: 25 start-page: 2483 year: 2013 end-page: 2493 article-title: Distributed autonomous online learning: regrets and intrinsic privacy‐preserving properties publication-title: IEEE Trans. Knowl. Data Eng. – year: 1996 – volume: 46 start-page: 667 year: 2004 end-page: 689 article-title: Fastest mixing Markov chain on a graph publication-title: SIAM Rev. – volume: 26 start-page: 1493 year: 2016 end-page: 1528 article-title: A Linearly Convergent Variant of the Conditional Gradient Algorithm under Strong Convexity, with Applications to Online and Stochastic Optimization publication-title: SIAM Journal on Optimization – year: 1990 – volume: 66 start-page: 3240 year: 2018 end-page: 3255 article-title: Decentralized online learning with kernels publication-title: IEEE Trans. Signal Process. – volume: 57 start-page: 592 year: 2012 end-page: 606 article-title: Dual averaging for distributed optimization: convergence analysis and network scaling publication-title: IEEE Trans. Autom. Control – volume: 393 start-page: 440 year: 1998 article-title: Collective dynamics of ‘small‐world’ networks publication-title: Nature – volume: 65 start-page: 2494 year: 2020 end-page: 2509 article-title: Asyspa: an exact asynchronous algorithm for convex optimization over digraphs publication-title: IEEE Trans. Autom. Control – volume: 101 start-page: 175 year: 2019 end-page: 181 article-title: Distributed quasi‐monotone subgradient algorithm for nonsmooth convex optimization over directed graphs publication-title: Automatica – volume: 57 start-page: 2821 year: 2019 end-page: 2842 article-title: Distributed subgradient‐free stochastic optimization algorithm for nonsmooth convex functions over time‐varying networks publication-title: SIAM J. Control Optim. – volume: 2 start-page: 183 year: 2009 end-page: 202 article-title: A fast iterative shrinkage‐thresholding algorithm for linear inverse problems publication-title: SIAM J. Imaging Sci. – volume: 14 start-page: 549 year: 2020 end-page: 557 article-title: Distributed optimisation based on multi‐agent system for resource allocation with communication time‐delay publication-title: IET Control Theory Appl. – volume: 1 start-page: 23 year: 2014 end-page: 37 article-title: Distributed online convex optimization over jointly connected digraphs publication-title: IEEE Trans. Netw. Sci. Eng. – volume: 18 start-page: 7942 year: 2017 end-page: 7946 article-title: SGDLibrary: A MATLAB library for stochastic optimization algorithms publication-title: J. Mach. Learn. Res. – volume: 3 start-page: 95 year: 1956 end-page: 110 article-title: An algorithm for quadratic programming publication-title: Nav. Res. Logist. Q. – volume: 59 start-page: 1131 year: 2014 end-page: 1146 article-title: Fast distributed gradient methods publication-title: IEEE Trans. Autom. Control – volume: 11 start-page: 2549 year: 2017 end-page: 2558 article-title: Distributed optimisation of second‐order multi‐agent systems by control algorithm using position‐only interaction with timevarying delay publication-title: IET Control Theory Appl. – volume: 69 start-page: 169 year: 2007 end-page: 192 article-title: Logarithmic regret algorithms for online convex optimization publication-title: Mach. Lear. – volume: 55 start-page: 922 year: 2010 end-page: 938 article-title: Constrained consensus and optimization in multi‐agent networks publication-title: IEEE Trans. Autom. Control – volume: 48 start-page: 1388 year: 2016 end-page: 1396 article-title: Gossip dual averaging for decentralized optimization of pairwise functions. In Proceedings of the 33rd International Conference on International Conference on Machine Learning (ICML 16) publication-title: JMLR.org – volume: 13 year: 1994 article-title: Interior‐point polynomial algorithms in convex programming publication-title: Soc. Ind. Appl. Math. – volume: 13 start-page: 2811 year: 2019 end-page: 2816 article-title: Distributed quadratic optimisation for linear multi‐agent systems over jointly connected networks publication-title: IET Control Theory Appl. – volume: 66 start-page: 682 year: 2017 end-page: 697 article-title: Node‐specific diffusion LMS‐based distributed detection over adaptive networks publication-title: IEEE Trans. Signal Process. – volume: 65 start-page: 2566 year: 2020 end-page: 2581 article-title: Accelerated distributed Nesterov gradient descent publication-title: IEEE Trans. Autom. Control – ident: e_1_2_7_5_1 doi: 10.1109/TSP.2018.2830299 – ident: e_1_2_7_10_1 doi: 10.1109/TAC.2008.2009515 – ident: e_1_2_7_18_1 doi: 10.1137/080716542 – volume: 48 start-page: 1388 year: 2016 ident: e_1_2_7_31_1 article-title: Gossip dual averaging for decentralized optimization of pairwise functions. In Proceedings of the 33rd International Conference on International Conference on Machine Learning (ICML 16) publication-title: JMLR.org – ident: e_1_2_7_29_1 doi: 10.1137/S0036144503423264 – ident: e_1_2_7_9_1 doi: 10.1109/TSP.2017.2771731 – ident: e_1_2_7_25_1 doi: 10.1109/TAC.2011.2161027 – ident: e_1_2_7_3_1 doi: 10.1049/iet-cta.2018.5585 – ident: e_1_2_7_13_1 doi: 10.1109/TNSE.2014.2363554 – ident: e_1_2_7_32_1 doi: 10.1137/18M119046X – start-page: 521 volume-title: International Conference on Machine Learning, Edinburg year: 2012 ident: e_1_2_7_22_1 – ident: e_1_2_7_15_1 doi: 10.1109/CDC.2013.6760092 – ident: e_1_2_7_11_1 doi: 10.1109/TAC.2014.2298712 – volume-title: Convex Analysis and Minimization Algorithms I' year: 1996 ident: e_1_2_7_28_1 – ident: e_1_2_7_2_1 doi: 10.1109/TKDE.2012.191 – volume-title: Matrix Analysis year: 1990 ident: e_1_2_7_26_1 – ident: e_1_2_7_19_1 doi: 10.1007/s10957-010-9737-7 – ident: e_1_2_7_4_1 doi: 10.1049/iet-cta.2019.0020 – start-page: 4054 volume-title: Proceedings of the 34th International Conference on Machine Learning, Sidney, August year: 2017 ident: e_1_2_7_16_1 – ident: e_1_2_7_17_1 doi: 10.1002/nav.3800030109 – ident: e_1_2_7_6_1 doi: 10.1109/TAC.2019.2937496 – volume: 18 start-page: 7942 year: 2017 ident: e_1_2_7_27_1 article-title: SGDLibrary: A MATLAB library for stochastic optimization algorithms publication-title: J. Mach. Learn. Res. – ident: e_1_2_7_12_1 doi: 10.1109/TCNS.2015.2505149 – ident: e_1_2_7_23_1 doi: 10.1137/140985366 – ident: e_1_2_7_30_1 doi: 10.1038/30918 – ident: e_1_2_7_20_1 doi: 10.1137/1.9781611970791 – volume: 6 start-page: 1595 year: 2005 ident: e_1_2_7_14_1 article-title: Decentralized online alternating direction method of multipliers publication-title: J. Comput. Appl. – ident: e_1_2_7_21_1 doi: 10.1007/s10994-007-5016-8 – ident: e_1_2_7_34_1 doi: 10.1002/rnc.5199 – ident: e_1_2_7_8_1 doi: 10.1049/iet-cta.2017.0064 – ident: e_1_2_7_7_1 doi: 10.1109/TAC.2019.2930234 – ident: e_1_2_7_24_1 doi: 10.1109/TAC.2010.2041686 – ident: e_1_2_7_33_1 doi: 10.1016/j.automatica.2018.11.056 |
| SSID | ssj0055645 |
| Score | 2.312362 |
| Snippet | This paper addresses a network of computing nodes aiming to solve an online convex optimisation problem in a distributed manner, that is, by means of the local... Abstract This paper addresses a network of computing nodes aiming to solve an online convex optimisation problem in a distributed manner, that is, by means of... |
| SourceID | doaj crossref wiley |
| SourceType | Open Website Enrichment Source Index Database Publisher |
| StartPage | 570 |
| SubjectTerms | Combinatorial mathematics Computational complexity Interpolation and function approximation (numerical analysis) Optimisation techniques Parallel programming and algorithm theory |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LSwMxEA4iHvQgPrG-COhFIbpNNpvNsYqlJ_FQobclTy20W2lXf7-TZFsURC_elmUgyzdhvpnN5BuELr22FohHEQiQkuRAWERBWkxywYwAfhJapmET4vGxHI3k05dRX6EnLMkDJ-Buge9U5oMCT-lyQbUWYXI1lb4ojSqoD9EXsp5lMZViMA8aKfEqJO-SEih_KUyay1vTvNKbLs0K-o2KomL_9ww1Ukx_B223uSHupW_aRWuu3kNbXxQD99FTD0MBa8fpDx5-mceOrQarycsMyvzXKYYkFNughhsGWTmLkxQGnkFomLZ3LvG4xnVq_14coOf-w_B-QNqhCMQwwSmhLgjgC-tkqQBP1jWl8ICqsj4TpjDCMco9ty4ceUE9lVHLlGLccOeYyxk7ROv1rHZHCFOdmYIblWlrcsm8NJp1FRc6tx7KON1BV0t8KtMqhofBFZMqnlznsgpYVhHLDrpY2b4lnYwfre4CzCuLoG0dX4DHq9bj1V8e76Dr6KRf1qnuhwMan47_Y8UTtElDK0tsPTtF68383Z2hDfPRjBfz87jzPgFe3tm9 priority: 102 providerName: Directory of Open Access Journals |
| Title | A conditional gradient algorithm for distributed online optimization in networks |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fcth2.12062 https://doaj.org/article/155a0f04378e472bb7654129f68ca62f |
| Volume | 15 |
| WOSCitedRecordID | wos000602729400001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1751-8652 dateEnd: 20241231 omitProxy: false ssIdentifier: ssj0055645 issn: 1751-8644 databaseCode: DOA dateStart: 20210101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 1751-8652 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055645 issn: 1751-8644 databaseCode: 24P dateStart: 20130101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 1751-8652 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055645 issn: 1751-8644 databaseCode: WIN dateStart: 20130101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JS8QwFA6iHvTgLo4bAb0oVNsskwa8qCh6Geag6K1kHQfGjtTq7_cl7YwKIoiXUsoLDXnJW5KX70Po0GtrwfGoBAykTBg4rERBWJwwQY0A_yS0bMgmRK-XPz7K_gw6m9yFafAhphtuYWVEex0WuNINCwkEtaBEUz-Rk4ykwQDPZRnNA3EDYf2JHeYBJyVeh-RZkoPbn4CTMnn62fabO4qo_d-j1Ohmrpf_18EVtNSGl_i8mQ-raMaVa2jxC-jgOuqfY8iB7bDZBMSDKhZ91ViNBuNqWD89Y4hjsQ2AuoELy1ncdAaPwbo8t9c28bDEZVNB_rqB7q-v7i5vkpZXITFUcJIQFzD0hXUyV6ASmplceFCMsj4VpmuEo4R7bl04NYOULCWWKkW54c5RxyjdRLPluHRbCBOdmi43KtXWMEm9NJpmigvNrIdMUHfQ0WR4C9OCjgfui1ERD7-ZLMIoFXGUOuhgKvvSQG38KHURtDSVCPDY8cO4GhTtaisgSFKpD7BNuWOCaC0C3TmRvpsb1SW-g46j5n75T3F5d0Pi2_ZfhHfQAglVL7FKbRfN1tWb20Pz5r0evlb7cYLux7wfng-3vQ_qIujN |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LSyNBEG6WuOB6cNcXRvfRoBcXRif9SE8f3bAhYTXkEDG3oZ8xoBMZR3-_XT2TqLAsyN6GoYZpurqeXfUVQsdeWxsMj0qCgpQJCwYrUcEtTpigRgT7JLSsh02I0SibTuW4qc2BXpgaH2KVcAPJiPoaBBwS0nXAyQAk01Q35LRDUtDAayx4GjC54Xo4WipiDkApsR-Sd5Is2P0lOimTZy_fvrFHEbb_rZsa7Uz_83-u8AvabBxMfF6fiC30wRXbaOMV7OAOGp_jEAXbeZ0GxLMyln1VWN3OFuW8urnDwZPFFiB1YRqWs7heDV4E_XLXNG7ieYGLuob8YRdd9X9PeoOkmayQGCo4SYgDFH1hncxUYArtmEz4wBplfSpM1whHCffcOrg3C0FZSixVinLDnaOOUbqHWsWicPsIE52aLjcq1dYwSb00mnYUF5pZH2JB3UYny_3NTQM7DtMvbvN4_c1kDruUx11qo6MV7X0NtvFXql_AphUFAGTHF4tyljfylgc3SaUegJsyxwTRWsDAcyJ9NzOqS3wb_Yys-8d_8t5kQOLTwXuIf6D1weTyIr8Yjv4cok8EamBizdpX1KrKR_cNfTRP1fyh_B5P6zNsaOsh |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NSxtBFB-KFrEHba1iqrYDelFY3Z2PzM5R0wZLJeSgkNsyn0kg2ci69u_vvNlNVJCCeFuWt-ww73vmvd9D6MRra4PjUUkwkDJhwWElKoTFCRPUiOCfhJbNsAkxGOSjkRy2tTnQC9PgQ6wO3EAzor0GBXf31jcJJwOQTFNPyHlGUrDA64yLDISasOHSEHMASon9kDxL8uD3l-ikTF48ffvCH0XY_pdhavQz_e13rvAz2moDTHzZSMQX9MGVO-jTM9jBr2h4iUMWbKfNMSAeV7Hsq8ZqNl5U03oyxyGSxRYgdWEalrO4WQ1eBPsybxs38bTEZVND_rCL7vq_bnvXSTtZITFUcJIQByj6wjqZq8AUmplc-MAaZX0qTNcIRwn33Dq4NwtJWUosVYpyw52jjlG6h9bKRen2ESY6NV1uVKqtYZJ6aTTNFBeaWR9yQd1Bp8v9LUwLOw7TL2ZFvP5msoBdKuIuddDxiva-Adt4leoK2LSiAIDs-GJRjYtW34oQJqnUA3BT7pggWgsYeE6k7-ZGdYnvoLPIuv_8p-jdXpP49O0txD_QxvBnv7j5PfhzgDYJlMDEkrVDtFZXj-4IfTR_6-lD9T0K6z-ehuo4 |
| 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=A+conditional+gradient+algorithm+for+distributed+online+optimization+in+networks&rft.jtitle=IET+control+theory+%26+applications&rft.au=Shen%2C+Xiuyu&rft.au=Li%2C+Dequan&rft.au=Fang%2C+Runyue&rft.au=Dong%2C+Qiao&rft.date=2021-03-01&rft.issn=1751-8644&rft.eissn=1751-8652&rft.volume=15&rft.issue=4&rft.spage=570&rft.epage=579&rft_id=info:doi/10.1049%2Fcth2.12062&rft.externalDBID=10.1049%252Fcth2.12062&rft.externalDocID=CTH212062 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1751-8644&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1751-8644&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1751-8644&client=summon |