Parallel subgradient algorithm with block dual decomposition for large-scale optimization
•Trade-off between minimizing the number of dualized constraints and detecting a structure amenable to parallel optimization.•Our approach accelerates the convergence of the distributed sub-gradient method when compared to the dual decomposition.•Denser constraint matrix leads to a higher number of...
Saved in:
| Published in: | European journal of operational research Vol. 299; no. 1; pp. 60 - 74 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Netherlands
Elsevier B.V
16.05.2022
|
| Subjects: | |
| ISSN: | 0377-2217, 1872-6860 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •Trade-off between minimizing the number of dualized constraints and detecting a structure amenable to parallel optimization.•Our approach accelerates the convergence of the distributed sub-gradient method when compared to the dual decomposition.•Denser constraint matrix leads to a higher number of dualized constraints and more iterations for convergence.•It is crucial to employ prior knowledge about the structure of the problem when solving large scale optimization problems.
This paper studies computational approaches for solving large-scale optimization problems using a Lagrangian dual reformulation, solved by parallel sub-gradient methods. Since there are many possible reformulations for a given problem, an important question is: Which reformulation leads to the fastest solution time? One approach is to detect a block diagonal structure in the constraint matrix, and reformulate the problem by dualizing the constraints outside of the blocks; the approach is defined herein as block dual decomposition. Main advantage of such a reformulation is that the Lagrangian relaxation has a block diagonal constraint matrix, thus decomposable into smaller sub-problems that can solved in parallel. We show that the block decomposition can critically affect convergence rate of the sub-gradient method. We propose various decomposition methods that use domain knowledge or apply algorithms using knowledge about the structure in the constraint matrix or the dependence in the decision variables, towards reducing the computational effort to solve large-scale optimization problems. In particular, we introduce a block decomposition approach that reduces the number of dualized constraints by utilizing a community detection algorithm. We present empirical experiments on an extensive set of problem instances including a real application. We illustrate that if the number of the dualized constraints in the decomposition increases, the computational effort within each iteration of the sub-gradient method decreases while the number of iterations required for convergence increases. The key message is that it is crucial to employ prior knowledge about the structure of the problem when solving large scale optimization problems using dual decomposition. |
|---|---|
| AbstractList | This paper studies computational approaches for solving large-scale optimization problems using a Lagrangian dual reformulation, solved by parallel sub-gradient methods. Since there are many possible reformulations for a given problem, an important question is: Which reformulation leads to the fastest solution time? One approach is to detect a block diagonal structure in the constraint matrix, and reformulate the problem by dualizing the constraints outside of the blocks; the approach is defined herein as block dual decomposition. Main advantage of such a reformulation is that the Lagrangian relaxation has a block diagonal constraint matrix, thus decomposable into smaller sub-problems that can solved in parallel. We show that the block decomposition can critically affect convergence rate of the sub-gradient method. We propose various decomposition methods that use domain knowledge or apply algorithms using knowledge about the structure in the constraint matrix or the dependence in the decision variables, towards reducing the computational effort to solve large-scale optimization problems. In particular, we introduce a block decomposition approach that reduces the number of dualized constraints by utilizing a community detection algorithm. We present empirical experiments on an extensive set of problem instances including a real application. We illustrate that if the number of the dualized constraints in the decomposition increases, the computational effort within each iteration of the sub-gradient method decreases while the number of iterations required for convergence increases. The key message is that it is crucial to employ prior knowledge about the structure of the problem when solving large scale optimization problems using dual decomposition.This paper studies computational approaches for solving large-scale optimization problems using a Lagrangian dual reformulation, solved by parallel sub-gradient methods. Since there are many possible reformulations for a given problem, an important question is: Which reformulation leads to the fastest solution time? One approach is to detect a block diagonal structure in the constraint matrix, and reformulate the problem by dualizing the constraints outside of the blocks; the approach is defined herein as block dual decomposition. Main advantage of such a reformulation is that the Lagrangian relaxation has a block diagonal constraint matrix, thus decomposable into smaller sub-problems that can solved in parallel. We show that the block decomposition can critically affect convergence rate of the sub-gradient method. We propose various decomposition methods that use domain knowledge or apply algorithms using knowledge about the structure in the constraint matrix or the dependence in the decision variables, towards reducing the computational effort to solve large-scale optimization problems. In particular, we introduce a block decomposition approach that reduces the number of dualized constraints by utilizing a community detection algorithm. We present empirical experiments on an extensive set of problem instances including a real application. We illustrate that if the number of the dualized constraints in the decomposition increases, the computational effort within each iteration of the sub-gradient method decreases while the number of iterations required for convergence increases. The key message is that it is crucial to employ prior knowledge about the structure of the problem when solving large scale optimization problems using dual decomposition. This paper studies computational approaches for solving large-scale optimization problems using a Lagrangian dual reformulation, solved by parallel sub-gradient methods. Since there are many possible reformulations for a given problem, an important question is: Which reformulation leads to the fastest solution time? One approach is to detect a block diagonal structure in the constraint matrix, and reformulate the problem by dualizing the constraints outside of the blocks; the approach is defined herein as block dual decomposition. Main advantage of such a reformulation is that the Lagrangian relaxation has a block diagonal constraint matrix, thus decomposable into smaller sub-problems that can solved in parallel. We show that the block decomposition can critically affect convergence rate of the sub-gradient method. We propose various decomposition methods that use domain knowledge or apply algorithms using knowledge about the structure in the constraint matrix or the dependence in the decision variables, towards reducing the computational effort to solve large-scale optimization problems. In particular, we introduce a block decomposition approach that reduces the number of dualized constraints by utilizing a community detection algorithm. We present empirical experiments on an extensive set of problem instances including a real application. We illustrate that if the number of the dualized constraints in the decomposition increases, the computational effort within each iteration of the sub-gradient method decreases while the number of iterations required for convergence increases. The key message is that it is crucial to employ prior knowledge about the structure of the problem when solving large scale optimization problems using dual decomposition. •Trade-off between minimizing the number of dualized constraints and detecting a structure amenable to parallel optimization.•Our approach accelerates the convergence of the distributed sub-gradient method when compared to the dual decomposition.•Denser constraint matrix leads to a higher number of dualized constraints and more iterations for convergence.•It is crucial to employ prior knowledge about the structure of the problem when solving large scale optimization problems. This paper studies computational approaches for solving large-scale optimization problems using a Lagrangian dual reformulation, solved by parallel sub-gradient methods. Since there are many possible reformulations for a given problem, an important question is: Which reformulation leads to the fastest solution time? One approach is to detect a block diagonal structure in the constraint matrix, and reformulate the problem by dualizing the constraints outside of the blocks; the approach is defined herein as block dual decomposition. Main advantage of such a reformulation is that the Lagrangian relaxation has a block diagonal constraint matrix, thus decomposable into smaller sub-problems that can solved in parallel. We show that the block decomposition can critically affect convergence rate of the sub-gradient method. We propose various decomposition methods that use domain knowledge or apply algorithms using knowledge about the structure in the constraint matrix or the dependence in the decision variables, towards reducing the computational effort to solve large-scale optimization problems. In particular, we introduce a block decomposition approach that reduces the number of dualized constraints by utilizing a community detection algorithm. We present empirical experiments on an extensive set of problem instances including a real application. We illustrate that if the number of the dualized constraints in the decomposition increases, the computational effort within each iteration of the sub-gradient method decreases while the number of iterations required for convergence increases. The key message is that it is crucial to employ prior knowledge about the structure of the problem when solving large scale optimization problems using dual decomposition. |
| Author | Zheng, Yuchen Serban, Nicoleta Lee, Ilbin Dehghanian, Amin Xie, Yujia |
| AuthorAffiliation | b Alberta School of Business, University of Alberta, 2-29B Business Building, Edmonton, Alberta T6G 2R6, Canada a H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Dr. NW Atlanta, GA 30332 |
| AuthorAffiliation_xml | – name: b Alberta School of Business, University of Alberta, 2-29B Business Building, Edmonton, Alberta T6G 2R6, Canada – name: a H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Dr. NW Atlanta, GA 30332 |
| Author_xml | – sequence: 1 givenname: Yuchen surname: Zheng fullname: Zheng, Yuchen email: yzheng67@gmail.com organization: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Dr. NW, Atlanta, GA 30332, USA – sequence: 2 givenname: Yujia surname: Xie fullname: Xie, Yujia email: yxie@gatech.edu organization: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Dr. NW, Atlanta, GA 30332, USA – sequence: 3 givenname: Ilbin orcidid: 0000-0003-1323-0393 surname: Lee fullname: Lee, Ilbin email: ilbin@ualberta.ca organization: Alberta School of Business, University of Alberta, 2-29B Business Building, Edmonton, Alberta T6G 2R6, Canada – sequence: 4 givenname: Amin surname: Dehghanian fullname: Dehghanian, Amin email: adehghanian3@gatech.edu organization: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Dr. NW, Atlanta, GA 30332, USA – sequence: 5 givenname: Nicoleta orcidid: 0000-0002-5813-7435 surname: Serban fullname: Serban, Nicoleta email: nserban@isye.gatech.edu organization: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 755 Ferst Dr. NW, Atlanta, GA 30332, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35035056$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9UU1v1DAQtVAR3Rb-AAeUI5eEsRPHjoSQUMWXVAkOcOBkOfZ468WJFztpBb8eL1sq4FDJGh_mvTcz752RkznOSMhTCg0F2r_YNbiLqWHAaENpA7x7QDZUClb3socTsoFWiJoxKk7JWc47AKCc8kfktOVQHu835OsnnXQIGKq8jtukrcd5qXTYxuSXq6m6KbUaQzTfKrvqUFk0cdrH7Bcf58rFVAWdtlhnowNWcb_4yf_Uh-Zj8tDpkPHJ7X9Ovrx98_nifX358d2Hi9eXteEMlto5AA6D6PjoNBohB255y52RnNtuYFYPnXHGUInCaOlGpmnLOUo3QDcitOfk1VF3v44TWlP2LxepffKTTj9U1F7925n9ldrGayUF79pBFIHntwIpfl8xL2ry2WAIesa4ZsV6BoLzAWSBPvt71t2QP34WADsCTIo5J3R3EArqEJraqUNo6hCaolSV0ApJ_kcyfvntYdnXh_upL49ULA5fe0wqm5KgQesTmkXZ6O-j_wI29bWY |
| CitedBy_id | crossref_primary_10_1016_j_trb_2025_103279 crossref_primary_10_1007_s11081_023_09838_4 crossref_primary_10_1287_ijoc_2022_0342 |
| Cites_doi | 10.1023/A:1009642405419 10.1016/j.ejor.2020.08.037 10.1109/TAC.2011.2161027 10.1016/j.compag.2011.01.006 10.1109/TNN.2005.845141 10.1016/0020-0190(92)90140-Q 10.1007/BF01584346 10.1016/j.physrep.2009.11.002 10.1007/s10107-014-0761-5 10.1007/BF02023050 10.1137/S1064827502401953 10.1016/j.tcs.2009.08.035 10.1073/pnas.0601602103 10.1109/TSMCA.2009.2025507 10.1007/s10107-015-0901-6 10.1007/s10107-004-0552-5 10.1007/s40745-015-0040-1 10.1111/1475-6773.12722 10.1287/opre.48.3.461.12439 10.1126/science.1136800 10.1103/PhysRevE.70.066111 10.1007/s12532-013-0061-8 10.1109/JSAC.2006.879350 10.1007/s10957-015-0758-0 10.1103/PhysRevE.69.066133 10.3182/20110828-6-IT-1002.01959 10.1109/TSP.2003.814623 10.1073/pnas.122653799 10.1287/ijoc.2017.0797 10.1016/S0167-6377(98)00050-9 10.1103/PhysRevE.69.026113 10.1109/TCOMM.2004.831346 10.1007/BF01582130 10.1016/0743-7315(90)90086-5 10.1126/science.1242072 10.1561/2200000016 10.1007/s10107-015-0892-3 10.1109/TAC.2008.2009515 10.1109/TKDE.2007.190689 10.1137/070708111 |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier B.V. |
| Copyright_xml | – notice: 2021 Elsevier B.V. |
| DBID | AAYXX CITATION NPM 7X8 5PM |
| DOI | 10.1016/j.ejor.2021.11.054 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science Business |
| EISSN | 1872-6860 |
| EndPage | 74 |
| ExternalDocumentID | PMC8754397 35035056 10_1016_j_ejor_2021_11_054 S0377221721010055 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NIDCR NIH HHS grantid: R01 DE028283 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 6OB 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABAOU ABBOA ABFNM ABFRF ABJNI ABMAC ABUCO ABYKQ ACAZW ACDAQ ACGFO ACGFS ACIWK ACNCT ACRLP ACZNC ADBBV ADEZE ADGUI AEBSH AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIGVJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR AXJTR BKOJK BKOMP BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W KOM LY1 M41 MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ RXW SCC SDF SDG SDP SDS SES SPC SPCBC SSB SSD SSV SSW SSZ T5K TAE TN5 U5U XPP ZMT ~02 ~G- 1OL 29G 41~ 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADIYS ADJOM ADMUD ADNMO ADXHL AEIPS AEUPX AFFNX AFJKZ AFPUW AGQPQ AI. AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB HVGLF HZ~ R2- SEW VH1 WUQ ~HD BNPGV NPM SSH 7X8 5PM |
| ID | FETCH-LOGICAL-c520t-ff00509745bfaec7895d535fc855d492da94cfcc18e7ca8fb2a1355e8f904be03 |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000743574900005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0377-2217 |
| IngestDate | Tue Sep 30 17:13:49 EDT 2025 Thu Oct 02 18:00:20 EDT 2025 Thu Apr 03 07:07:24 EDT 2025 Sat Nov 29 07:19:12 EST 2025 Tue Nov 18 20:47:29 EST 2025 Fri Feb 23 02:40:58 EST 2024 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Block dual decomposition Large scale optimization Community detection Parallel subgradient algorithm Distributed decision making Block Dual Decomposition Parallel Subgradient Algorithm |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c520t-ff00509745bfaec7895d535fc855d492da94cfcc18e7ca8fb2a1355e8f904be03 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-5813-7435 0000-0003-1323-0393 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/8754397 |
| PMID | 35035056 |
| PQID | 2620755908 |
| PQPubID | 23479 |
| PageCount | 15 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_8754397 proquest_miscellaneous_2620755908 pubmed_primary_35035056 crossref_primary_10_1016_j_ejor_2021_11_054 crossref_citationtrail_10_1016_j_ejor_2021_11_054 elsevier_sciencedirect_doi_10_1016_j_ejor_2021_11_054 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-05-16 |
| PublicationDateYYYYMMDD | 2022-05-16 |
| PublicationDate_xml | – month: 05 year: 2022 text: 2022-05-16 day: 16 |
| PublicationDecade | 2020 |
| PublicationPlace | Netherlands |
| PublicationPlace_xml | – name: Netherlands |
| PublicationTitle | European journal of operational research |
| PublicationTitleAlternate | Eur J Oper Res |
| PublicationYear | 2022 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Medhi (bib0032) 1990; 22 Goffin (bib0023) 1977; 13 Brandes, Delling, Gaertler, Gorke, Hoefer, Nikoloski, Wagner (bib0009) 2007; 20 Simonetto, Jamali-Rad (bib0047) 2016; 168 Boyd (bib0007) 2014 Khaniyev, Elhedhli, Erenay (bib0027) 2018; 30 Wright (bib0050) 2015; 151 Newman (bib0037) 2006; 103 Shastri, Hansen, Rodríguez, Ting (bib0046) 2011; 76 Raffard, Tomlin, Boyd (bib0042) 2004; vol. 3 Hromkovič (bib0025) 2013 Gentili, Serban, Harati, O’Connor, Swann (bib0021) 2017; 53 Holmberg, Yuan (bib0024) 2000; 48 Androulakis, Visweswaran, Floudas (bib0001) 1996 Wolfe, Haghighi, Klein (bib0049) 2008 Girvan, Newman (bib0022) 2002; 99 Bertsekas (bib0005) 2011; 2010 Aykanat, Pinar, Çatalyürek (bib0002) 2004; 25 Nedic, Ozdaglar (bib0034) 2009; 54 Maher (bib0030) 2021; 290 Boyd, Parikh, Chu, Peleato, Eckstein (bib0008) 2011; 3 Xiao, Johansson, Boyd (bib0051) 2004; 52 Campegiani, Presti (bib0011) 2009 Rehfeldt, Hobbie, Schönheit, Koch, Möst, Gleixner (bib0043) 2021; in press Richtárik, Takáč (bib0044) 2016; 156 Xu, Tian (bib0052) 2015; 2 Newman (bib0036) 2004; 69 Parikh, Boyd (bib0041) 2014; 6 Nedic, Ozdaglar (bib0033) 2009; 19 Rodriguez, Laio (bib0045) 2014; 344 Bui, Jones (bib0010) 1992; 42 Duchi, Hazan, Singer (bib0016) 2011; 12 Knobe, Lukas, Steele (bib0028) 1990; 8 Nesterov (bib0035) 2005; 103 Lyaudet (bib0029) 2010; 411 Martin (bib0031) 1999 Newman, Girvan (bib0038) 2004; 69 Bezanson, J., Karpinski, S., Shah, V. B., & Edelman, A. (2012). Julia: A fast dynamic language for technical computing. arXiv preprint arXiv:1209.5145,. Palomar, Mung (bib0040) 2006; 24 Nowak (bib0039) 2003; 51 Chu, Beasley (bib0014) 1998; 4 Bertsekas (bib0004) 1995 Bergner, Caprara, Ceselli, Furini, Lübbecke, Malaguti, Traversi (bib0003) 2015; 149 Xu, Wunsch (bib0053) 2005; 16 Camponogara, De Oliveira (bib0012) 2009; 39 Frey, Dueck (bib0020) 2007; 315 Clauset, Newman, Moore (bib0015) 2004; 70 Duchi, Agarwal, Wainwright (bib0017) 2012; 57 Ferris, Horn (bib0018) 1998; 80 Fortunato (bib0019) 2010; 486 Terelius, Topcu, Murray (bib0048) 2011; 44 Carøe, Schultz (bib0013) 1999; 24 Inalhan, Stipanovic, Tomlin (bib0026) 2002; vol. 1 Gentili (10.1016/j.ejor.2021.11.054_bib0021) 2017; 53 Nowak (10.1016/j.ejor.2021.11.054_bib0039) 2003; 51 Shastri (10.1016/j.ejor.2021.11.054_bib0046) 2011; 76 Wright (10.1016/j.ejor.2021.11.054_bib0050) 2015; 151 Inalhan (10.1016/j.ejor.2021.11.054_bib0026) 2002; vol. 1 Frey (10.1016/j.ejor.2021.11.054_bib0020) 2007; 315 Wolfe (10.1016/j.ejor.2021.11.054_bib0049) 2008 Bergner (10.1016/j.ejor.2021.11.054_bib0003) 2015; 149 Campegiani (10.1016/j.ejor.2021.11.054_bib0011) 2009 Girvan (10.1016/j.ejor.2021.11.054_bib0022) 2002; 99 Carøe (10.1016/j.ejor.2021.11.054_bib0013) 1999; 24 Duchi (10.1016/j.ejor.2021.11.054_bib0016) 2011; 12 Aykanat (10.1016/j.ejor.2021.11.054_bib0002) 2004; 25 Lyaudet (10.1016/j.ejor.2021.11.054_bib0029) 2010; 411 10.1016/j.ejor.2021.11.054_bib0006 Medhi (10.1016/j.ejor.2021.11.054_bib0032) 1990; 22 Xu (10.1016/j.ejor.2021.11.054_bib0053) 2005; 16 Newman (10.1016/j.ejor.2021.11.054_bib0037) 2006; 103 Chu (10.1016/j.ejor.2021.11.054_bib0014) 1998; 4 Newman (10.1016/j.ejor.2021.11.054_bib0036) 2004; 69 Maher (10.1016/j.ejor.2021.11.054_bib0030) 2021; 290 Clauset (10.1016/j.ejor.2021.11.054_bib0015) 2004; 70 Hromkovič (10.1016/j.ejor.2021.11.054_bib0025) 2013 Camponogara (10.1016/j.ejor.2021.11.054_bib0012) 2009; 39 Knobe (10.1016/j.ejor.2021.11.054_bib0028) 1990; 8 Xiao (10.1016/j.ejor.2021.11.054_bib0051) 2004; 52 Goffin (10.1016/j.ejor.2021.11.054_bib0023) 1977; 13 Androulakis (10.1016/j.ejor.2021.11.054_bib0001) 1996 Duchi (10.1016/j.ejor.2021.11.054_bib0017) 2012; 57 Khaniyev (10.1016/j.ejor.2021.11.054_bib0027) 2018; 30 Nesterov (10.1016/j.ejor.2021.11.054_bib0035) 2005; 103 Boyd (10.1016/j.ejor.2021.11.054_bib0008) 2011; 3 Terelius (10.1016/j.ejor.2021.11.054_bib0048) 2011; 44 Ferris (10.1016/j.ejor.2021.11.054_bib0018) 1998; 80 Rodriguez (10.1016/j.ejor.2021.11.054_bib0045) 2014; 344 Simonetto (10.1016/j.ejor.2021.11.054_bib0047) 2016; 168 Newman (10.1016/j.ejor.2021.11.054_bib0038) 2004; 69 Nedic (10.1016/j.ejor.2021.11.054_bib0034) 2009; 54 Rehfeldt (10.1016/j.ejor.2021.11.054_bib0043) 2021; in press Parikh (10.1016/j.ejor.2021.11.054_bib0041) 2014; 6 Martin (10.1016/j.ejor.2021.11.054_bib0031) 1999 Richtárik (10.1016/j.ejor.2021.11.054_bib0044) 2016; 156 Boyd (10.1016/j.ejor.2021.11.054_bib0007) 2014 Raffard (10.1016/j.ejor.2021.11.054_bib0042) 2004; vol. 3 Nedic (10.1016/j.ejor.2021.11.054_bib0033) 2009; 19 Xu (10.1016/j.ejor.2021.11.054_bib0052) 2015; 2 Holmberg (10.1016/j.ejor.2021.11.054_bib0024) 2000; 48 Bertsekas (10.1016/j.ejor.2021.11.054_bib0005) 2011; 2010 Bui (10.1016/j.ejor.2021.11.054_bib0010) 1992; 42 Palomar (10.1016/j.ejor.2021.11.054_bib0040) 2006; 24 Fortunato (10.1016/j.ejor.2021.11.054_bib0019) 2010; 486 Brandes (10.1016/j.ejor.2021.11.054_bib0009) 2007; 20 Bertsekas (10.1016/j.ejor.2021.11.054_bib0004) 1995 |
| References_xml | – volume: 69 start-page: 026113 year: 2004 ident: bib0038 article-title: Finding and evaluating community structure in networks publication-title: Physical Review E – volume: 168 start-page: 172 year: 2016 end-page: 197 ident: bib0047 article-title: Primal recovery from consensus-based dual decomposition for distributed convex optimization publication-title: Journal of Optimization Theory and Applications – volume: 344 start-page: 1492 year: 2014 end-page: 1496 ident: bib0045 article-title: Clustering by fast search and find of density peaks publication-title: Science – volume: in press year: 2021 ident: bib0043 article-title: A massively parallel interior-point solver for LPs with generalized arrowhead structure, and applications to energy system models publication-title: European Journal of Operational Research – volume: 6 start-page: 77 year: 2014 end-page: 102 ident: bib0041 article-title: Block splitting for distributed optimization publication-title: Mathematical Programming Computation – volume: 3 start-page: 1 year: 2011 end-page: 122 ident: bib0008 article-title: Distributed optimization and statistical learning via the alternating direction method of multipliers publication-title: Foundations and Trends in Machine Learning – volume: 315 start-page: 972 year: 2007 end-page: 976 ident: bib0020 article-title: Clustering by passing messages between data points publication-title: Science – year: 1999 ident: bib0031 article-title: Large scale linear and integer optimization: A unified approach – volume: 19 start-page: 1757 year: 2009 end-page: 1780 ident: bib0033 article-title: Approximate primal solutions and rate analysis for dual subgradient methods publication-title: SIAM Journal on Optimization – volume: 12 start-page: 2121 year: 2011 end-page: 2159 ident: bib0016 article-title: Adaptive subgradient methods for online learning and stochastic optimization publication-title: Journal of Machine Learning Research – volume: 411 start-page: 10 year: 2010 end-page: 21 ident: bib0029 article-title: NP-hard and linear variants of hypergraph partitioning publication-title: Theoretical Computer Science – volume: 8 start-page: 102 year: 1990 end-page: 118 ident: bib0028 article-title: Data optimization: Allocation of arrays to reduce communication on SIMD machines publication-title: Journal of parallel and Distributed Computing – reference: Bezanson, J., Karpinski, S., Shah, V. B., & Edelman, A. (2012). Julia: A fast dynamic language for technical computing. arXiv preprint arXiv:1209.5145,. – volume: 24 start-page: 37 year: 1999 end-page: 45 ident: bib0013 article-title: Dual decomposition in stochastic integer programming publication-title: Operations Research Letters – volume: 48 start-page: 461 year: 2000 end-page: 481 ident: bib0024 article-title: A Lagrangian heuristic based branch-and-bound approach for the capacitated network design problem publication-title: Operations Research – volume: vol. 3 start-page: 2453 year: 2004 end-page: 2459 ident: bib0042 article-title: Distributed optimization for cooperative agents: Application to formation flight publication-title: Proceedings of the 43rd IEEE conference on decision and control, 2004 CDC – volume: 99 start-page: 7821 year: 2002 end-page: 7826 ident: bib0022 article-title: Community structure in social and biological networks publication-title: Proceedings of the National Academy of Sciences – volume: vol. 1 start-page: 1147 year: 2002 end-page: 1155 ident: bib0026 article-title: Decentralized optimization, with application to multiple aircraft coordination publication-title: Proceedings of the 41st IEEE conference on decision and control, 2002. – volume: 52 start-page: 1136 year: 2004 end-page: 1144 ident: bib0051 article-title: Simultaneous routing and resource allocation via dual decomposition publication-title: IEEE Transactions on Communications – year: 1995 ident: bib0004 article-title: Nonlinear programming – start-page: 285 year: 1996 end-page: 301) ident: bib0001 article-title: Distributed decomposition-based approaches in global optimization publication-title: State of the art in global optimization: Computational methods and applications – volume: 20 start-page: 172 year: 2007 end-page: 188 ident: bib0009 article-title: On modularity clustering publication-title: IEEE Transactions on Knowledge and Data Engineering – volume: 13 start-page: 329 year: 1977 end-page: 347 ident: bib0023 article-title: On convergence rates of subgradient optimization methods publication-title: Mathematical Programming – volume: 22 start-page: 101 year: 1990 end-page: 127 ident: bib0032 article-title: Parallel bundle-based decomposition for large-scale structured mathematical programming problems publication-title: Annals of Operations Research – volume: 2 start-page: 165 year: 2015 end-page: 193 ident: bib0052 article-title: A comprehensive survey of clustering algorithms publication-title: Annals of Data Science – start-page: 1184 year: 2008 end-page: 1191 ident: bib0049 article-title: Fully distributed em for very large datasets publication-title: Proceedings of the 25th international conference on machine learning – volume: 51 start-page: 2245 year: 2003 end-page: 2253 ident: bib0039 article-title: Distributed em algorithms for density estimation and clustering in sensor networks publication-title: IEEE Transactions on Signal Processing – volume: 54 start-page: 48 year: 2009 end-page: 61 ident: bib0034 article-title: Distributed subgradient methods for multi-agent optimization publication-title: IEEE Transactions on Automatic Control – volume: 30 start-page: 570 year: 2018 end-page: 587 ident: bib0027 article-title: Structure detection in mixed-integer programs publication-title: INFORMS Journal on Computing – volume: 16 start-page: 645 year: 2005 end-page: 678 ident: bib0053 article-title: Survey of clustering algorithms publication-title: IEEE Transactions on Neural Networks – volume: 290 start-page: 479 year: 2021 end-page: 498 ident: bib0030 article-title: Implementing the branch-and-cut approach for a general purpose Benders decomposition framework publication-title: European Journal of Operational Research – volume: 44 start-page: 11245 year: 2011 end-page: 11251 ident: bib0048 article-title: Decentralized multi-agent optimization via dual decomposition publication-title: IFAC Proceedings Volumes – volume: 4 start-page: 63 year: 1998 end-page: 86 ident: bib0014 article-title: A genetic algorithm for the multidimensional knapsack problem publication-title: Journal of Heuristics – year: 2014 ident: bib0007 article-title: Subgradient methods – volume: 70 start-page: 066111 year: 2004 ident: bib0015 article-title: Finding community structure in very large networks publication-title: Physical Review E – volume: 42 start-page: 153 year: 1992 end-page: 159 ident: bib0010 article-title: Finding good approximate vertex and edge partitions is NP-hard publication-title: Information Processing Letters – volume: 57 start-page: 592 year: 2012 end-page: 606 ident: bib0017 article-title: Dual averaging for distributed optimization: Convergence analysis and network scaling publication-title: IEEE Transactions on Automatic Control – volume: 486 start-page: 75 year: 2010 end-page: 174 ident: bib0019 article-title: Community detection in graphs publication-title: Physics Reports – year: 2013 ident: bib0025 article-title: Communication complexity and parallel computing – volume: 25 start-page: 1860 year: 2004 end-page: 1879 ident: bib0002 article-title: Permuting sparse rectangular matrices into block-diagonal form publication-title: SIAM Journal on Scientific Computing – volume: 156 start-page: 433 year: 2016 end-page: 484 ident: bib0044 article-title: Parallel coordinate descent methods for big data optimization publication-title: Mathematical Programming – volume: 39 start-page: 1331 year: 2009 end-page: 1338 ident: bib0012 article-title: Distributed optimization for model predictive control of linear-dynamic networks publication-title: IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans – volume: 103 start-page: 127 year: 2005 end-page: 152 ident: bib0035 article-title: Smooth minimization of non-smooth functions publication-title: Mathematical Programming – start-page: 162 year: 2009 end-page: 167 ident: bib0011 article-title: A general model for virtual machines resources allocation in multi-tier distributed systems publication-title: Proceedings of the fifth international conference on autonomic and autonomous systems – volume: 53 start-page: 1458 year: 2017 end-page: 1477 ident: bib0021 article-title: Quantifying disparities in accessibility and availability of pediatric primary care with implications for policy publication-title: Health Services Research – volume: 69 start-page: 066133 year: 2004 ident: bib0036 article-title: Fast algorithm for detecting community structure in networks publication-title: Physical Review E – volume: 80 start-page: 35 year: 1998 end-page: 61 ident: bib0018 article-title: Partitioning mathematical programs for parallel solution publication-title: Mathematical Programming – volume: 151 start-page: 3 year: 2015 end-page: 34 ident: bib0050 article-title: Coordinate descent algorithms publication-title: Mathematical Programming – volume: 149 start-page: 391 year: 2015 end-page: 424 ident: bib0003 article-title: Automatic Dantzig–Wolfe reformulation of mixed integer programs publication-title: Mathematical Programming – volume: 103 start-page: 8577 year: 2006 end-page: 8582 ident: bib0037 article-title: Modularity and community structure in networks publication-title: Proceedings of the National Academy of Sciences – volume: 2010 start-page: 3 year: 2011 ident: bib0005 article-title: Incremental gradient, subgradient, and proximal methods for convex optimization: A survey publication-title: Optimization for Machine Learning – volume: 24 start-page: 1439 year: 2006 end-page: 1451 ident: bib0040 article-title: A tutorial on decomposition methods for network utility maximization publication-title: IEEE Journal on Selected Areas in Communications – volume: 76 start-page: 69 year: 2011 end-page: 79 ident: bib0046 article-title: A novel decomposition and distributed computing approach for the solution of large scale optimization models publication-title: Computers and Electronics in Agriculture – volume: 4 start-page: 63 issue: 1 year: 1998 ident: 10.1016/j.ejor.2021.11.054_bib0014 article-title: A genetic algorithm for the multidimensional knapsack problem publication-title: Journal of Heuristics doi: 10.1023/A:1009642405419 – volume: 290 start-page: 479 issue: 2 year: 2021 ident: 10.1016/j.ejor.2021.11.054_bib0030 article-title: Implementing the branch-and-cut approach for a general purpose Benders decomposition framework publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2020.08.037 – volume: 57 start-page: 592 issue: 3 year: 2012 ident: 10.1016/j.ejor.2021.11.054_bib0017 article-title: Dual averaging for distributed optimization: Convergence analysis and network scaling publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2011.2161027 – volume: 76 start-page: 69 issue: 1 year: 2011 ident: 10.1016/j.ejor.2021.11.054_bib0046 article-title: A novel decomposition and distributed computing approach for the solution of large scale optimization models publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2011.01.006 – year: 1999 ident: 10.1016/j.ejor.2021.11.054_bib0031 – volume: 16 start-page: 645 issue: 3 year: 2005 ident: 10.1016/j.ejor.2021.11.054_bib0053 article-title: Survey of clustering algorithms publication-title: IEEE Transactions on Neural Networks doi: 10.1109/TNN.2005.845141 – volume: 42 start-page: 153 issue: 3 year: 1992 ident: 10.1016/j.ejor.2021.11.054_bib0010 article-title: Finding good approximate vertex and edge partitions is NP-hard publication-title: Information Processing Letters doi: 10.1016/0020-0190(92)90140-Q – volume: 13 start-page: 329 issue: 1 year: 1977 ident: 10.1016/j.ejor.2021.11.054_bib0023 article-title: On convergence rates of subgradient optimization methods publication-title: Mathematical Programming doi: 10.1007/BF01584346 – volume: 486 start-page: 75 issue: 3 year: 2010 ident: 10.1016/j.ejor.2021.11.054_bib0019 article-title: Community detection in graphs publication-title: Physics Reports doi: 10.1016/j.physrep.2009.11.002 – volume: 149 start-page: 391 issue: 1-2 year: 2015 ident: 10.1016/j.ejor.2021.11.054_bib0003 article-title: Automatic Dantzig–Wolfe reformulation of mixed integer programs publication-title: Mathematical Programming doi: 10.1007/s10107-014-0761-5 – volume: 22 start-page: 101 issue: 1 year: 1990 ident: 10.1016/j.ejor.2021.11.054_bib0032 article-title: Parallel bundle-based decomposition for large-scale structured mathematical programming problems publication-title: Annals of Operations Research doi: 10.1007/BF02023050 – volume: 25 start-page: 1860 issue: 6 year: 2004 ident: 10.1016/j.ejor.2021.11.054_bib0002 article-title: Permuting sparse rectangular matrices into block-diagonal form publication-title: SIAM Journal on Scientific Computing doi: 10.1137/S1064827502401953 – volume: 411 start-page: 10 issue: 1 year: 2010 ident: 10.1016/j.ejor.2021.11.054_bib0029 article-title: NP-hard and linear variants of hypergraph partitioning publication-title: Theoretical Computer Science doi: 10.1016/j.tcs.2009.08.035 – volume: 2010 start-page: 3 issue: 1–38 year: 2011 ident: 10.1016/j.ejor.2021.11.054_bib0005 article-title: Incremental gradient, subgradient, and proximal methods for convex optimization: A survey publication-title: Optimization for Machine Learning – volume: in press year: 2021 ident: 10.1016/j.ejor.2021.11.054_bib0043 article-title: A massively parallel interior-point solver for LPs with generalized arrowhead structure, and applications to energy system models publication-title: European Journal of Operational Research – volume: 103 start-page: 8577 issue: 23 year: 2006 ident: 10.1016/j.ejor.2021.11.054_bib0037 article-title: Modularity and community structure in networks publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.0601602103 – volume: 39 start-page: 1331 issue: 6 year: 2009 ident: 10.1016/j.ejor.2021.11.054_bib0012 article-title: Distributed optimization for model predictive control of linear-dynamic networks publication-title: IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans doi: 10.1109/TSMCA.2009.2025507 – volume: vol. 1 start-page: 1147 year: 2002 ident: 10.1016/j.ejor.2021.11.054_bib0026 article-title: Decentralized optimization, with application to multiple aircraft coordination – volume: 156 start-page: 433 issue: 1-2 year: 2016 ident: 10.1016/j.ejor.2021.11.054_bib0044 article-title: Parallel coordinate descent methods for big data optimization publication-title: Mathematical Programming doi: 10.1007/s10107-015-0901-6 – volume: 103 start-page: 127 issue: 1 year: 2005 ident: 10.1016/j.ejor.2021.11.054_bib0035 article-title: Smooth minimization of non-smooth functions publication-title: Mathematical Programming doi: 10.1007/s10107-004-0552-5 – volume: 12 start-page: 2121 issue: Jul year: 2011 ident: 10.1016/j.ejor.2021.11.054_bib0016 article-title: Adaptive subgradient methods for online learning and stochastic optimization publication-title: Journal of Machine Learning Research – volume: 2 start-page: 165 issue: 2 year: 2015 ident: 10.1016/j.ejor.2021.11.054_bib0052 article-title: A comprehensive survey of clustering algorithms publication-title: Annals of Data Science doi: 10.1007/s40745-015-0040-1 – year: 2013 ident: 10.1016/j.ejor.2021.11.054_bib0025 – volume: 53 start-page: 1458 issue: 3 year: 2017 ident: 10.1016/j.ejor.2021.11.054_bib0021 article-title: Quantifying disparities in accessibility and availability of pediatric primary care with implications for policy publication-title: Health Services Research doi: 10.1111/1475-6773.12722 – volume: 48 start-page: 461 issue: 3 year: 2000 ident: 10.1016/j.ejor.2021.11.054_bib0024 article-title: A Lagrangian heuristic based branch-and-bound approach for the capacitated network design problem publication-title: Operations Research doi: 10.1287/opre.48.3.461.12439 – year: 1995 ident: 10.1016/j.ejor.2021.11.054_bib0004 – year: 2014 ident: 10.1016/j.ejor.2021.11.054_bib0007 – volume: 315 start-page: 972 issue: 5814 year: 2007 ident: 10.1016/j.ejor.2021.11.054_bib0020 article-title: Clustering by passing messages between data points publication-title: Science doi: 10.1126/science.1136800 – start-page: 162 year: 2009 ident: 10.1016/j.ejor.2021.11.054_bib0011 article-title: A general model for virtual machines resources allocation in multi-tier distributed systems – volume: 70 start-page: 066111 issue: 6 year: 2004 ident: 10.1016/j.ejor.2021.11.054_bib0015 article-title: Finding community structure in very large networks publication-title: Physical Review E doi: 10.1103/PhysRevE.70.066111 – volume: 6 start-page: 77 issue: 1 year: 2014 ident: 10.1016/j.ejor.2021.11.054_bib0041 article-title: Block splitting for distributed optimization publication-title: Mathematical Programming Computation doi: 10.1007/s12532-013-0061-8 – volume: 24 start-page: 1439 issue: 8 year: 2006 ident: 10.1016/j.ejor.2021.11.054_bib0040 article-title: A tutorial on decomposition methods for network utility maximization publication-title: IEEE Journal on Selected Areas in Communications doi: 10.1109/JSAC.2006.879350 – volume: 168 start-page: 172 issue: 1 year: 2016 ident: 10.1016/j.ejor.2021.11.054_bib0047 article-title: Primal recovery from consensus-based dual decomposition for distributed convex optimization publication-title: Journal of Optimization Theory and Applications doi: 10.1007/s10957-015-0758-0 – volume: 69 start-page: 066133 issue: 6 year: 2004 ident: 10.1016/j.ejor.2021.11.054_bib0036 article-title: Fast algorithm for detecting community structure in networks publication-title: Physical Review E doi: 10.1103/PhysRevE.69.066133 – volume: 44 start-page: 11245 issue: 1 year: 2011 ident: 10.1016/j.ejor.2021.11.054_bib0048 article-title: Decentralized multi-agent optimization via dual decomposition publication-title: IFAC Proceedings Volumes doi: 10.3182/20110828-6-IT-1002.01959 – volume: 51 start-page: 2245 issue: 8 year: 2003 ident: 10.1016/j.ejor.2021.11.054_bib0039 article-title: Distributed em algorithms for density estimation and clustering in sensor networks publication-title: IEEE Transactions on Signal Processing doi: 10.1109/TSP.2003.814623 – volume: 99 start-page: 7821 issue: 12 year: 2002 ident: 10.1016/j.ejor.2021.11.054_bib0022 article-title: Community structure in social and biological networks publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.122653799 – volume: 30 start-page: 570 issue: 3 year: 2018 ident: 10.1016/j.ejor.2021.11.054_bib0027 article-title: Structure detection in mixed-integer programs publication-title: INFORMS Journal on Computing doi: 10.1287/ijoc.2017.0797 – volume: 24 start-page: 37 issue: 1–2 year: 1999 ident: 10.1016/j.ejor.2021.11.054_bib0013 article-title: Dual decomposition in stochastic integer programming publication-title: Operations Research Letters doi: 10.1016/S0167-6377(98)00050-9 – volume: vol. 3 start-page: 2453 year: 2004 ident: 10.1016/j.ejor.2021.11.054_bib0042 article-title: Distributed optimization for cooperative agents: Application to formation flight – start-page: 285 year: 1996 ident: 10.1016/j.ejor.2021.11.054_bib0001 article-title: Distributed decomposition-based approaches in global optimization – volume: 69 start-page: 026113 issue: 2 year: 2004 ident: 10.1016/j.ejor.2021.11.054_bib0038 article-title: Finding and evaluating community structure in networks publication-title: Physical Review E doi: 10.1103/PhysRevE.69.026113 – volume: 52 start-page: 1136 issue: 7 year: 2004 ident: 10.1016/j.ejor.2021.11.054_bib0051 article-title: Simultaneous routing and resource allocation via dual decomposition publication-title: IEEE Transactions on Communications doi: 10.1109/TCOMM.2004.831346 – volume: 80 start-page: 35 issue: 1 year: 1998 ident: 10.1016/j.ejor.2021.11.054_bib0018 article-title: Partitioning mathematical programs for parallel solution publication-title: Mathematical Programming doi: 10.1007/BF01582130 – volume: 8 start-page: 102 issue: 2 year: 1990 ident: 10.1016/j.ejor.2021.11.054_bib0028 article-title: Data optimization: Allocation of arrays to reduce communication on SIMD machines publication-title: Journal of parallel and Distributed Computing doi: 10.1016/0743-7315(90)90086-5 – volume: 344 start-page: 1492 issue: 6191 year: 2014 ident: 10.1016/j.ejor.2021.11.054_bib0045 article-title: Clustering by fast search and find of density peaks publication-title: Science doi: 10.1126/science.1242072 – volume: 3 start-page: 1 issue: 1 year: 2011 ident: 10.1016/j.ejor.2021.11.054_bib0008 article-title: Distributed optimization and statistical learning via the alternating direction method of multipliers publication-title: Foundations and Trends in Machine Learning doi: 10.1561/2200000016 – volume: 151 start-page: 3 issue: 1 year: 2015 ident: 10.1016/j.ejor.2021.11.054_bib0050 article-title: Coordinate descent algorithms publication-title: Mathematical Programming doi: 10.1007/s10107-015-0892-3 – ident: 10.1016/j.ejor.2021.11.054_bib0006 – volume: 54 start-page: 48 issue: 1 year: 2009 ident: 10.1016/j.ejor.2021.11.054_bib0034 article-title: Distributed subgradient methods for multi-agent optimization publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2008.2009515 – volume: 20 start-page: 172 issue: 2 year: 2007 ident: 10.1016/j.ejor.2021.11.054_bib0009 article-title: On modularity clustering publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2007.190689 – volume: 19 start-page: 1757 issue: 4 year: 2009 ident: 10.1016/j.ejor.2021.11.054_bib0033 article-title: Approximate primal solutions and rate analysis for dual subgradient methods publication-title: SIAM Journal on Optimization doi: 10.1137/070708111 – start-page: 1184 year: 2008 ident: 10.1016/j.ejor.2021.11.054_bib0049 article-title: Fully distributed em for very large datasets |
| SSID | ssj0001515 |
| Score | 2.4053578 |
| Snippet | •Trade-off between minimizing the number of dualized constraints and detecting a structure amenable to parallel optimization.•Our approach accelerates the... This paper studies computational approaches for solving large-scale optimization problems using a Lagrangian dual reformulation, solved by parallel... |
| SourceID | pubmedcentral proquest pubmed crossref elsevier |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 60 |
| SubjectTerms | Block dual decomposition Community detection Distributed decision making Large scale optimization Parallel subgradient algorithm |
| Title | Parallel subgradient algorithm with block dual decomposition for large-scale optimization |
| URI | https://dx.doi.org/10.1016/j.ejor.2021.11.054 https://www.ncbi.nlm.nih.gov/pubmed/35035056 https://www.proquest.com/docview/2620755908 https://pubmed.ncbi.nlm.nih.gov/PMC8754397 |
| Volume | 299 |
| WOSCitedRecordID | wos000743574900005&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-6860 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001515 issn: 0377-2217 databaseCode: AIEXJ dateStart: 19950105 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9owFLYonaruYRd2Y5fKk_aGUpGLsfPIRqd1qro-dBLsJXISB8JCQCFUfdtf33FsZ4GKanvYS4RsJ0R8H-ccH58LQh9sOvC5nYSWcGGL4tkc5CDoIcsOOXPj2OYiqerMXtDLSzYe-1et1i-TC3OT0Txnt7f-6r9CDWMAtkyd_Qe464fCAHwG0OEKsMP1r4C_4oXsj5JJmTAtqoiusjfMpssiLWcL5Xj9CCrsZ28kU0dGQkaV69CtKujwQgaHW2sAT_S-gURZ6FTNvU58bdDCQGFci7qGUO1r_jETSqhMNsCSmpBjdToy2czTWj3o0KDzLEzrdSMxm854nipv7XChZ7S3AkCXhU4HDaHmUmo5jsrXNBLYUT2Stqim5KnqNXBHzCuPw_xUzJeypqtjn8pKrKoadQPi1aLC2CXy-JTsVNxWOlxPHaBDhxKftdHh8Pxs_LVW5tLeqw6i9GvrvCsVIrj79cfoyDxwn5lzdxuzG43bMG-un6BHel-Ch4pPT1FL5B10ZNIiOuixaf-BtTbooIeNWpbP0MTwDjd4h2veYck7XPEOS97hLd5h4B1u8A43efccff98dv3pi6X7dlgRcfqllSRVVSHqkTDhIqLMJzFxZbAgIbHnOzH3vSiJIpsJGnGWhA63wewVLPH7Xij67gvUzpe5eIUwY2HSDx3BaRx5oI1CKugg5oRRHkehP-gi2_zMQaSL2sveKllgohfngUQpkCjBbjcAlLqoV9-zUiVd7l1NDHqBNkqVsRkAGe-9772BOgCJLY_heC6Wm3UgW0BQ2Mj3WRe9VNDX72Ho00V0ixT1AlkNfnsmT2dVVXhGidxcvN77zDfo-M8_8i1ql8VGvEMPopsyXRcn6ICO2Ymm_2_DhdKS |
| linkProvider | Elsevier |
| 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=Parallel+Subgradient+Algorithm+with+Block+Dual+Decomposition+for+Large-scale+Optimization&rft.jtitle=European+journal+of+operational+research&rft.au=Zheng%2C+Yuchen&rft.au=Xie%2C+Yujia&rft.au=Lee%2C+Ilbin&rft.au=Dehghanian%2C+Amin&rft.date=2022-05-16&rft.issn=0377-2217&rft.volume=299&rft.issue=1&rft.spage=60&rft_id=info:doi/10.1016%2Fj.ejor.2021.11.054&rft_id=info%3Apmid%2F35035056&rft.externalDocID=35035056 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0377-2217&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0377-2217&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0377-2217&client=summon |