Solving Pseudomonotone Variational Inequalities and Pseudoconvex Optimization Problems Using the Projection Neural Network
In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalitie...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on neural networks Jg. 17; H. 6; S. 1487 - 1499 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York, NY
IEEE
01.11.2006
Institute of Electrical and Electronics Engineers |
| Schlagworte: | |
| ISSN: | 1045-9227, 1941-0093 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalities and related pseudoconvex optimization problems. Under various pseudomonotonicity conditions and other conditions, the projection neural network is proved to be stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable. Since monotonicity is a special case of pseudomononicity, the projection neural network can be applied to solve a broader class of constrained optimization problems related to variational inequalities. Moreover, a new concept, called componentwise pseudomononicity, different from pseudomononicity in general, is introduced. Under this new concept, two stability results of the projection neural network for solving variational inequalities are also obtained. Finally, numerical examples show the effectiveness and performance of the projection neural network |
|---|---|
| AbstractList | In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalities and related pseudoconvex optimization problems. Under various pseudomonotonicity conditions and other conditions, the projection neural network is proved to be stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable. Since monotonicity is a special case of pseudomononicity, the projection neural network can be applied to solve a broader class of constrained optimization problems related to variational inequalities. Moreover, a new concept, called componentwise pseudomononicity, different from pseudomononicity in general, is introduced. Under this new concept, two stability results of the projection neural network for solving variational inequalities are also obtained. Finally, numerical examples show the effectiveness and performance of the projection neural network In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalities and related pseudoconvex optimization problems. Under various pseudomonotonicity conditions and other conditions, the projection neural network is proved to be stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable. Since monotonicity is a special case of pseudomononicity, the projection neural network can be applied to solve a broader class of constrained optimization problems related to variational inequalities. Moreover, a new concept, called componentwise pseudomononicity, different from pseudomononicity in general, is introduced. Under this new concept, two stability results of the projection neural network for solving variational inequalities are also obtained. Finally, numerical examples show the effectiveness and performance of the projection neural network.In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalities and related pseudoconvex optimization problems. Under various pseudomonotonicity conditions and other conditions, the projection neural network is proved to be stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable. Since monotonicity is a special case of pseudomononicity, the projection neural network can be applied to solve a broader class of constrained optimization problems related to variational inequalities. Moreover, a new concept, called componentwise pseudomononicity, different from pseudomononicity in general, is introduced. Under this new concept, two stability results of the projection neural network for solving variational inequalities are also obtained. Finally, numerical examples show the effectiveness and performance of the projection neural network. In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalities and related pseudoconvex optimization problems. Under various pseudomonotonicity conditions and other conditions, the projection neural network is proved to be stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable. Since monotonicity is a special case of pseudomononicity, the projection neural network can be applied to solve a broader class of constrained optimization problems related to variational inequalities. Moreover, a new concept, called componentwise pseudomononicity, different from pseudomononicity in general, is introduced. Under this new concept, two stability results of the projection neural network for solving variational inequalities are also obtained. Finally, numerical examples show the effectiveness and performance of the projection neural network. |
| Author | Xiaolin Hu Jun Wang |
| Author_xml | – sequence: 1 givenname: Xiaolin surname: Hu fullname: Hu, Xiaolin organization: Department of Automation and Computer-Aided Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China – sequence: 2 givenname: Jun surname: Wang fullname: Wang, Jun |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18295788$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/17131663$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kc1rFTEUxYNU7IeuXQgyG3U1r0kmk4-lFKuF8lqwdTvkJXc0dSZ5TTK19q83897TgotCIOHmd86Fcw7Rng8eEHpN8IIQrI6vlssFxZgvpFBCsGfogChGaoxVs1femLW1olTso8OUbjAmrMX8BdongjSE8-YAPXwNw53z36vLBJMNY_AhlxXVNx2dzi54PVRnHm4nPbjsIFXa2x1rgr-D--pind3oHjZwdRnDaoAxVddpNs0_YB7dgNn8LmGKxW8J-VeIP1-i570eErza3Ufo-vTT1cmX-vzi89nJx_PaMMxyrawSlkjVclBWGmEaJhVTWpkeWMvUSoqVLaen2GLKLZNaY9PTpue4lcQ0R-jD1ncdw-0EKXejSwaGQXsIU-qk4iUiwVUh3z9JckmUaFpZwLc7cFqNYLt1dKOOv7u_uRbg3Q7Qyeihj9oblx45SVUr5Gx0vOVMDClF6B8R3M0Nd6Xhbm642zZcFO1_CuPyJvwctRue0L3Z6hwA_NvCMKGYiuYPhV20Zw |
| CODEN | ITNNEP |
| CitedBy_id | crossref_primary_10_1109_TCYB_2016_2611529 crossref_primary_10_1137_23M1627304 crossref_primary_10_1109_TNN_2010_2048123 crossref_primary_10_1109_TNNLS_2015_2496658 crossref_primary_10_3233_JIFS_220972 crossref_primary_10_1002_acs_3804 crossref_primary_10_1109_TNN_2007_910736 crossref_primary_10_1016_j_ins_2023_120078 crossref_primary_10_1109_TNNLS_2016_2575860 crossref_primary_10_1109_TETCI_2024_3369667 crossref_primary_10_1109_TNNLS_2017_2731325 crossref_primary_10_1016_j_cam_2024_116420 crossref_primary_10_1109_TNNLS_2015_2461553 crossref_primary_10_1109_TPAMI_2009_141 crossref_primary_10_1109_COMST_2024_3363639 crossref_primary_10_1016_j_neucom_2020_02_100 crossref_primary_10_1016_j_neucom_2010_03_009 crossref_primary_10_1155_2014_283092 crossref_primary_10_1109_TNN_2008_2003287 crossref_primary_10_1109_TCYB_2019_2925707 crossref_primary_10_1002_rnc_5403 crossref_primary_10_1016_j_neucom_2008_02_016 crossref_primary_10_1007_s41478_022_00384_3 crossref_primary_10_1007_s11590_022_01874_w crossref_primary_10_1109_TCYB_2021_3093076 crossref_primary_10_1109_TNNLS_2021_3126730 crossref_primary_10_1080_02331934_2022_2094795 crossref_primary_10_1109_TAC_2015_2416927 crossref_primary_10_1109_TSMC_2021_3050993 crossref_primary_10_1186_s13660_024_03099_0 crossref_primary_10_1007_s40314_024_02606_9 crossref_primary_10_1155_2020_8818794 crossref_primary_10_1002_asjc_3609 crossref_primary_10_1002_mma_10147 crossref_primary_10_3390_sym13020182 crossref_primary_10_1080_02331934_2024_2385645 crossref_primary_10_1016_j_neunet_2018_01_008 crossref_primary_10_1016_j_neucom_2022_07_034 crossref_primary_10_1109_TCYB_2016_2567449 crossref_primary_10_1016_j_neucom_2013_09_028 crossref_primary_10_1002_rnc_6340 crossref_primary_10_1109_TIE_2011_2169636 crossref_primary_10_1016_j_automatica_2023_111203 crossref_primary_10_1016_j_cam_2024_116448 crossref_primary_10_1080_02331934_2021_1928123 crossref_primary_10_1109_TCYB_2020_3031379 crossref_primary_10_1109_TNN_2008_2006263 crossref_primary_10_1007_s11067_023_09606_y crossref_primary_10_1016_j_neunet_2013_11_007 crossref_primary_10_1007_s12559_017_9495_z crossref_primary_10_1109_TNNLS_2015_2500618 crossref_primary_10_1109_TNN_2008_2000273 crossref_primary_10_1080_0305215X_2025_2450683 crossref_primary_10_1109_TNNLS_2016_2582381 crossref_primary_10_1109_TNNLS_2022_3213167 crossref_primary_10_1109_TSMC_2023_3274222 crossref_primary_10_1016_j_neucom_2021_04_059 crossref_primary_10_1016_j_neucom_2012_07_043 crossref_primary_10_1007_s10489_014_0616_z crossref_primary_10_1155_2022_9447175 crossref_primary_10_1007_s11075_019_00718_6 crossref_primary_10_1016_j_eswa_2024_123980 crossref_primary_10_1109_TNNLS_2015_2441697 crossref_primary_10_1109_TNN_2008_2011266 crossref_primary_10_1109_TNNLS_2015_2466612 crossref_primary_10_1007_s40747_020_00265_x crossref_primary_10_1109_TNNLS_2014_2387862 crossref_primary_10_1007_s10114_022_0243_2 crossref_primary_10_1016_j_neunet_2014_06_002 crossref_primary_10_1109_TNNLS_2020_3008661 crossref_primary_10_1016_j_neucom_2018_03_016 crossref_primary_10_1109_TNNLS_2013_2275732 crossref_primary_10_1016_j_neunet_2013_11_015 crossref_primary_10_1080_00207721_2024_2428844 crossref_primary_10_1016_j_jfranklin_2021_11_034 crossref_primary_10_1016_j_neunet_2021_10_007 crossref_primary_10_1007_s11768_023_00181_8 crossref_primary_10_1016_j_engappai_2020_104115 crossref_primary_10_1016_j_neucom_2024_127518 crossref_primary_10_1080_01630563_2022_2069813 crossref_primary_10_1016_j_neunet_2019_12_015 crossref_primary_10_1007_s11075_023_01746_z crossref_primary_10_1007_s11590_018_1230_5 crossref_primary_10_1016_j_artmed_2008_03_003 crossref_primary_10_1016_j_neucom_2020_04_023 crossref_primary_10_1109_TNNLS_2020_3027288 crossref_primary_10_1016_j_neunet_2024_106247 crossref_primary_10_1109_TNNLS_2016_2570257 crossref_primary_10_1007_s40314_024_02699_2 crossref_primary_10_1080_02331934_2021_1909584 crossref_primary_10_1016_j_neunet_2011_09_001 crossref_primary_10_1080_0952813X_2019_1647559 crossref_primary_10_1007_s10440_008_9402_4 crossref_primary_10_1016_j_neunet_2015_09_013 crossref_primary_10_1016_j_neucom_2024_128988 crossref_primary_10_1109_TCSII_2012_2228400 crossref_primary_10_1109_ACCESS_2020_3028125 crossref_primary_10_1109_TNNLS_2015_2425301 crossref_primary_10_1007_s10957_020_01792_w crossref_primary_10_1109_TNNLS_2021_3105732 crossref_primary_10_1007_s11075_024_01851_7 crossref_primary_10_1007_s11590_020_01654_4 crossref_primary_10_1007_s12190_024_02186_1 crossref_primary_10_1109_TAC_2022_3214795 crossref_primary_10_1007_s11590_019_01511_z crossref_primary_10_1080_02331934_2018_1522636 crossref_primary_10_1016_j_neunet_2021_01_004 crossref_primary_10_1186_s13660_023_03046_5 crossref_primary_10_1007_s00521_012_0918_1 crossref_primary_10_1109_TII_2022_3180080 crossref_primary_10_1016_j_neucom_2013_01_025 crossref_primary_10_1109_TNN_2011_2169682 crossref_primary_10_1016_j_cnsns_2013_08_034 crossref_primary_10_1109_TNNLS_2018_2814824 crossref_primary_10_1016_j_neucom_2013_10_008 crossref_primary_10_1016_j_neunet_2021_04_038 crossref_primary_10_1109_TNNLS_2013_2244908 crossref_primary_10_1016_j_neucom_2014_08_014 crossref_primary_10_1007_s10462_009_9111_z crossref_primary_10_1007_s10915_020_01327_5 crossref_primary_10_1109_TNNLS_2021_3082528 crossref_primary_10_1016_j_neunet_2014_09_009 crossref_primary_10_1109_TFUZZ_2020_3031385 crossref_primary_10_1109_TSMC_2019_2916750 crossref_primary_10_1007_s40065_022_00400_1 crossref_primary_10_1186_s13660_019_2233_1 crossref_primary_10_1007_s11063_021_10538_2 crossref_primary_10_1016_j_neunet_2019_02_002 crossref_primary_10_1080_00036811_2024_2330509 crossref_primary_10_1080_02331934_2021_1963248 crossref_primary_10_1016_j_cam_2022_114260 crossref_primary_10_1016_j_neucom_2011_04_026 crossref_primary_10_1007_s40747_022_00884_6 crossref_primary_10_1016_j_amc_2012_01_020 crossref_primary_10_1007_s11063_010_9129_x crossref_primary_10_1007_s40314_023_02582_6 crossref_primary_10_1016_j_neucom_2019_01_012 crossref_primary_10_1080_02331934_2019_1613404 crossref_primary_10_1007_s11590_020_01599_8 crossref_primary_10_1007_s11063_021_10628_1 crossref_primary_10_1016_j_cam_2018_08_030 crossref_primary_10_1016_j_neucom_2019_05_078 crossref_primary_10_1016_j_neunet_2018_03_003 crossref_primary_10_1016_j_neunet_2014_03_006 crossref_primary_10_1109_TNN_2007_891593 crossref_primary_10_3390_axioms9040115 crossref_primary_10_1016_j_asoc_2019_01_002 crossref_primary_10_1109_TNN_2008_2000993 crossref_primary_10_1016_j_neunet_2021_01_012 crossref_primary_10_1007_s10589_010_9359_x crossref_primary_10_1109_TNNLS_2019_2944388 crossref_primary_10_1016_j_ins_2009_11_014 crossref_primary_10_1109_TNN_2011_2109735 crossref_primary_10_1109_TNNLS_2022_3144148 crossref_primary_10_1080_02331934_2020_1849206 crossref_primary_10_1155_2019_4545064 crossref_primary_10_1016_j_cnsns_2024_108315 crossref_primary_10_1016_j_jfranklin_2023_11_041 crossref_primary_10_3390_math8071165 crossref_primary_10_1109_TNNLS_2015_2404773 crossref_primary_10_1109_TNNLS_2013_2292893 crossref_primary_10_1515_ijnsns_2021_0459 crossref_primary_10_1016_j_cam_2022_114092 crossref_primary_10_1109_TNN_2011_2167760 crossref_primary_10_1007_s11067_019_09457_6 crossref_primary_10_1007_s11075_019_00780_0 crossref_primary_10_1016_j_neucom_2012_01_020 crossref_primary_10_1016_j_chaos_2008_05_003 crossref_primary_10_1109_JAS_2020_1003605 crossref_primary_10_1049_cth2_12147 crossref_primary_10_1016_j_amc_2010_02_010 crossref_primary_10_1016_j_neucom_2009_05_003 crossref_primary_10_3390_sym16030363 |
| Cites_doi | 10.1023/A:1004611224835 10.1007/BF00941891 10.1080/0233193021000030779 10.1007/BF00339943 10.1023/A:1012606212823 10.1109/ICARCV.2006.345414 10.1109/31.52732 10.1137/S0036144595285963 10.1016/S0893-9659(01)00137-9 10.1287/opre.42.6.1120 10.1137/S0363012994268655 10.1109/TNN.2004.841779 10.1109/81.995659 10.1023/B:JOTA.0000042598.21226.af 10.1109/TCSI.2004.830694 10.1109/72.914529 10.1016/0893-6080(94)90041-8 10.1016/S0096-3003(01)00269-7 10.1109/31.1783 10.1016/j.neunet.2004.05.006 10.1007/BF01585696 10.1016/S0893-6080(00)00019-8 10.1023/A:1017562305308 10.1007/BF01582255 10.1016/j.jmaa.2004.04.068 10.1109/31.41295 10.1007/BF01580617 10.1007/BF00940531 10.1007/BF01582565 10.1109/TCS.1986.1085953 |
| ContentType | Journal Article |
| Copyright | 2007 INIST-CNRS |
| Copyright_xml | – notice: 2007 INIST-CNRS |
| DBID | 97E RIA RIE AAYXX CITATION IQODW CGR CUY CVF ECM EIF NPM 7X8 7SC 7SP 8FD F28 FR3 JQ2 L7M L~C L~D |
| DOI | 10.1109/TNN.2006.879774 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Pascal-Francis Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering 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 MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database MEDLINE - Academic MEDLINE |
| 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: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Anatomy & Physiology Computer Science Applied Sciences |
| EISSN | 1941-0093 |
| EndPage | 1499 |
| ExternalDocumentID | 17131663 18295788 10_1109_TNN_2006_879774 4012027 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | --- -~X .DC 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E AAJGR AASAJ AAWTH ABAZT ABJNI ABQJQ ABVLG ACGFS AETIX AGQYO AGSQL AHBIQ AI. AIBXA ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P RIA RIE RNS S10 TAE TN5 VH1 AAYXX CITATION IQODW RIG AAYOK CGR CUY CVF ECM EIF NPM PKN Z5M 7X8 7SC 7SP 8FD F28 FR3 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c404t-9d97d18956e9d8c7c348949a9cfe4549b87bd7bdf20d026d48aa0cf23f60581c3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 238 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000241933100012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1045-9227 |
| IngestDate | Fri Sep 05 10:52:45 EDT 2025 Fri Sep 05 07:44:33 EDT 2025 Wed Feb 19 01:47:08 EST 2025 Mon Jul 21 09:15:37 EDT 2025 Tue Nov 18 22:11:23 EST 2025 Sat Nov 29 03:59:20 EST 2025 Tue Aug 26 16:36:05 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 6 |
| Keywords | Monotonic function Recurrent neural nets Componentwise pseudomonotone variational inequality global asymptotic stability pseudoconvex optimization Lyapunov method Neural network Convex programming Constrained optimization projection neural network Asymptotic stability Variational inequality pseudomonotone variational inequality Monotonicity Lyapunov function Mathematical programming |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html CC BY 4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c404t-9d97d18956e9d8c7c348949a9cfe4549b87bd7bdf20d026d48aa0cf23f60581c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 |
| PMID | 17131663 |
| PQID | 68197358 |
| PQPubID | 23479 |
| PageCount | 13 |
| ParticipantIDs | crossref_primary_10_1109_TNN_2006_879774 pubmed_primary_17131663 ieee_primary_4012027 crossref_citationtrail_10_1109_TNN_2006_879774 pascalfrancis_primary_18295788 proquest_miscellaneous_896227769 proquest_miscellaneous_68197358 |
| PublicationCentury | 2000 |
| PublicationDate | 2006-11-01 |
| PublicationDateYYYYMMDD | 2006-11-01 |
| PublicationDate_xml | – month: 11 year: 2006 text: 2006-11-01 day: 01 |
| PublicationDecade | 2000 |
| PublicationPlace | New York, NY |
| PublicationPlace_xml | – name: New York, NY – name: United States |
| PublicationTitle | IEEE transactions on neural networks |
| PublicationTitleAbbrev | TNN |
| PublicationTitleAlternate | IEEE Trans Neural Netw |
| PublicationYear | 2006 |
| Publisher | IEEE Institute of Electrical and Electronics Engineers |
| Publisher_xml | – name: IEEE – name: Institute of Electrical and Electronics Engineers |
| References | ref13 ref12 ref36 ref14 ref31 ref33 hu (ref30) 2006 ref11 ref32 ref10 ref39 ref17 ref19 kojima (ref38) 1986; 29 ref18 cottle (ref2) 1980 kosko (ref15) 1992 ref24 ref23 ref26 kinderlehrer (ref1) 1980 ref25 ref20 ref22 ref21 ref28 avriel (ref35) 1988; 36 ref27 miller (ref34) 1982 noor (ref29) 2002; 31 ref8 ref7 hopfield (ref16) 1985; 52 ref9 ref4 ref3 ref6 ref5 bazaraa (ref37) 1993 |
| References_xml | – ident: ref25 doi: 10.1023/A:1004611224835 – volume: 29 start-page: 352 year: 1986 ident: ref38 article-title: extensions of newton and quasi-newton methods to systems of equations publication-title: J Oper Res Soc Japan – ident: ref33 doi: 10.1007/BF00941891 – ident: ref36 doi: 10.1080/0233193021000030779 – volume: 52 start-page: 141 year: 1985 ident: ref16 article-title: neural computation of decisions in optimization problems publication-title: Biol Cybern doi: 10.1007/BF00339943 – ident: ref12 doi: 10.1023/A:1012606212823 – ident: ref14 doi: 10.1109/ICARCV.2006.345414 – ident: ref19 doi: 10.1109/31.52732 – ident: ref6 doi: 10.1137/S0036144595285963 – year: 1982 ident: ref34 publication-title: Ordinary Differential Equations – year: 1992 ident: ref15 publication-title: Neural Networks for Signal Processing – ident: ref13 doi: 10.1016/S0893-9659(01)00137-9 – ident: ref5 doi: 10.1287/opre.42.6.1120 – ident: ref9 doi: 10.1137/S0363012994268655 – ident: ref23 doi: 10.1109/TNN.2004.841779 – ident: ref27 doi: 10.1109/81.995659 – ident: ref28 doi: 10.1023/B:JOTA.0000042598.21226.af – ident: ref21 doi: 10.1109/TCSI.2004.830694 – ident: ref26 doi: 10.1109/72.914529 – ident: ref20 doi: 10.1016/0893-6080(94)90041-8 – year: 1980 ident: ref2 publication-title: Variational Inequalities and Complementarity Problems Theorey and Applications – ident: ref31 doi: 10.1016/S0096-3003(01)00269-7 – ident: ref18 doi: 10.1109/31.1783 – ident: ref22 doi: 10.1016/j.neunet.2004.05.006 – ident: ref7 doi: 10.1007/BF01585696 – ident: ref24 doi: 10.1016/S0893-6080(00)00019-8 – start-page: 755 year: 2006 ident: ref30 article-title: global stability of a recurrent neural network for solving pseudomonotone variational inequalities publication-title: Proc IEEE Int Symp Circuits Syst – year: 1993 ident: ref37 publication-title: Nonlinear Programming Theory and Algorithms – ident: ref11 doi: 10.1023/A:1017562305308 – ident: ref4 doi: 10.1007/BF01582255 – volume: 36 year: 1988 ident: ref35 publication-title: Generalized concavity Mathematical Concepts and Methods in Science and Engineering – ident: ref10 doi: 10.1016/j.jmaa.2004.04.068 – volume: 31 start-page: 173 year: 2002 ident: ref29 article-title: a wiener-hopf dynamical system for variational inequalities publication-title: New Zealand J Math – ident: ref3 doi: 10.1109/31.41295 – ident: ref39 doi: 10.1007/BF01580617 – ident: ref32 doi: 10.1007/BF00940531 – year: 1980 ident: ref1 publication-title: An Introduction to Variational Inequalities and Their Applications – ident: ref8 doi: 10.1007/BF01582565 – ident: ref17 doi: 10.1109/TCS.1986.1085953 |
| SSID | ssj0014506 |
| Score | 2.3522234 |
| Snippet | In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex... |
| SourceID | proquest pubmed pascalfrancis crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1487 |
| SubjectTerms | Algorithms Applied sciences Artificial intelligence Artificial neural networks Asymptotic properties Asymptotic stability Circuits Componentwise pseudomonotone variational inequality Computer science; control theory; systems Connectionism. Neural networks Constraint optimization Constraints Convergence Exact sciences and technology global asymptotic stability Inequalities Information Storage and Retrieval - methods Iterative algorithms Neural networks Neural Networks (Computer) Optimization Pattern Recognition, Automated - methods Projection projection neural network pseudoconvex optimization pseudomonotone variational inequality Recurrent neural networks Signal processing algorithms Signal Processing, Computer-Assisted Stability Telecommunication traffic |
| Title | Solving Pseudomonotone Variational Inequalities and Pseudoconvex Optimization Problems Using the Projection Neural Network |
| URI | https://ieeexplore.ieee.org/document/4012027 https://www.ncbi.nlm.nih.gov/pubmed/17131663 https://www.proquest.com/docview/68197358 https://www.proquest.com/docview/896227769 |
| Volume | 17 |
| WOSCitedRecordID | wos000241933100012&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 Electronic Library (IEL) customDbUrl: eissn: 1941-0093 dateEnd: 20111231 omitProxy: false ssIdentifier: ssj0014506 issn: 1045-9227 databaseCode: RIE dateStart: 19900101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9tAEB4B6oEeCk1a6rake6hQDzX4sd7HESEQvZhI0Co3y95dS63ARjhBtL--s4-EIjUHpBysaLJ2PDO7880T4DOeSkmjUQEVWhsxZayJRatNLPGobDltlMn8sAlelmI2k9MN-LqqhTHGuOQzc2gvXSxf92phXWVH1FZ6ZnwTNjnnvlZrFTGghZujieiiwBtlPLTxSRN5dFWWPuoguLV2bJ9QhGYpY_mTw8hNV7G5kfWAr6f1cy3WG57uADrbed6j78KrYGiSYy8Zr2HDdCMYH3cIsm9-kwPiUj-dT30EO8vZDiSo-ghe_tOocAx_Lvtr63kg08EsdI-i29sm3uQHIu3gTSTfOuMrNBF7k7rTgdaltT-QC9yabkLNJ5n6KTYDcQkLBG1Q-9UvlxXWEdsvBNcrfYL6G_h-dnp1ch6HqQ2xogmdx1JLrlOBuMtILRRXORWSylqq1lBEo43gjcZPmyUaAaCmoq4T1WZ5ayO0qcrfwlaHf-EdkMJuKEWTWnJa01YWhjOW1fguqTFFFsHhkn2VCi3N7WSN68pBm0RWyHo7aJNVnvURfFn94NZ381hPOrZcXJEFBkYweSIfj8uITOLuJyL4tBSYCjXVhl_qzvSLoWJofPG8QAqyhkJIhpLLmYxgz4va4_JBYt___7E-wLbzDbkiyY-wNb9bmH14oe7nP4e7CerLTEycvvwF5M0SFw |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6VggQcKOzyCI_WB4Q4kDZxHD-OFaJqRQkrsaDeosR2JFCboGYXAb-e8WO3VGIPSDlE0cR5zNieb54AL3FXylqDE1CjtpEyzttUdsamCrfKTrBWWxqaTYiqkmdnarYFb9a5MNZaH3xm992p9-WbQS-dqeyAuUxPKm7AzZIxmodsrbXPgJW-kybiixIfRUUs5JNn6mBeVcHvIIXTd1ylUARnOefFte3I91dx0ZHNiD-oC50tNquefgs62vm_l78P96KqSQ6DbDyALdtPYHrYI8y--EVeER_86a3qE9hZdXcgcbJP4O5fpQqn8PvTcO5sD2Q22qUZUHgHV8abfEGsHe2J5KS3IUcT0TdpehNpfWD7T_IRF6eLmPVJZqGPzUh8yAJBLdRd-ubjwnriKobgeFUIUX8In4_ezd8ep7FvQ6pZxhapMkqYXCLysspILXTBpGKqUbqzDPFoK0Vr8OhoZhACGiabJtMdLTrno8118Qi2e_yEJ0BKt6SUbe7IWcM6VVrBOW3wXzJrS5rA_op9tY5FzV1vjfPag5tM1ch612qT14H1Cbxe3_A91PPYTDp1XFyTRQYmsHtNPq6GkVTh-icT2FsJTI1z1Tlgmt4Oy7HmqH6JokQKsoFCKo6SK7hK4HEQtavho8Q-_fdr7cHt4_mH0_r0pHr_DO54S5FPmXwO24vLpX0Bt_SPxdfxctfPmj_KbBR2 |
| 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=Solving+pseudomonotone+variational+inequalities+and+pseudoconvex+optimization+problems+using+the+projection+neural+network&rft.jtitle=IEEE+transactions+on+neural+networks&rft.au=XIAOLIN+HU&rft.au=JUN+WANG&rft.date=2006-11-01&rft.pub=Institute+of+Electrical+and+Electronics+Engineers&rft.issn=1045-9227&rft.volume=17&rft.issue=6&rft.spage=1487&rft.epage=1499&rft_id=info:doi/10.1109%2FTNN.2006.879774&rft.externalDBID=n%2Fa&rft.externalDocID=18295788 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1045-9227&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1045-9227&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1045-9227&client=summon |