DC approximation approaches for sparse optimization

•A unifying DC approximation, including all standard approximations, of the zero-norm is proposed.•The consistency between global/local minima of approximate and original problems are proved.•The equivalence between approximate and original problems are established for some approximations.•Four DCA...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	European journal of operational research Ročník 244; číslo 1; s. 26 - 46
Hlavní autoři:	Le Thi, H.A., Pham Dinh, T., Le, H.M., Vo, X.T.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.07.2015 Elsevier Sequoia S.A Elsevier
Témata:	Algorithms Approximation Computer Science DC approximation function DC programming and DCA Direct current Feature selection in SVM Global optimization Mathematical analysis Mathematical functions Mathematical models Mathematical problems Mathematical programming Optimization Optimization algorithms Programming Sparse optimization Studies Sparse optimization DC programming and DCA Global optimization DC approximation function Feature selection in SVM
ISSN:	0377-2217, 1872-6860
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	•A unifying DC approximation, including all standard approximations, of the zero-norm is proposed.•The consistency between global/local minima of approximate and original problems are proved.•The equivalence between approximate and original problems are established for some approximations.•Four DCA schemes are developed that cover all standard nonconvex approximation algorithms.•A careful empirical experiment for feature selection in SVM are performed. Sparse optimization refers to an optimization problem involving the zero-norm in objective or constraints. In this paper, nonconvex approximation approaches for sparse optimization have been studied with a unifying point of view in DC (Difference of Convex functions) programming framework. Considering a common DC approximation of the zero-norm including all standard sparse inducing penalty functions, we studied the consistency between global minimums (resp. local minimums) of approximate and original problems. We showed that, in several cases, some global minimizers (resp. local minimizers) of the approximate problem are also those of the original problem. Using exact penalty techniques in DC programming, we proved stronger results for some particular approximations, namely, the approximate problem, with suitable parameters, is equivalent to the original problem. The efficiency of several sparse inducing penalty functions have been fully analyzed. Four DCA (DC Algorithm) schemes were developed that cover all standard algorithms in nonconvex sparse approximation approaches as special versions. They can be viewed as, an ℓ1-perturbed algorithm/reweighted-ℓ1 algorithm / reweighted-ℓ2 algorithm. We offer a unifying nonconvex approximation approach, with solid theoretical tools as well as efficient algorithms based on DC programming and DCA, to tackle the zero-norm and sparse optimization. As an application, we implemented our methods for the feature selection in SVM (Support Vector Machine) problem and performed empirical comparative numerical experiments on the proposed algorithms with various approximation functions.
AbstractList	Sparse optimization refers to an optimization problem involving the zero-norm in objective or constraints. In this paper, nonconvex approximation approaches for sparse optimization have been studied with a unifying point of view in DC (Difference of Convex functions) programming framework. Considering a common DC approximation of the zero-norm including all standard sparse inducing penalty functions, we studied the consistency between global minimums (resp. local minimums) of approximate and original problems. We showed that, in several cases, some global minimizers (resp. local minimizers) of the approximate problem are also those of the original problem. Using exact penalty techniques in DC programming, we proved stronger results for some particular approximations, namely, the approximate problem, with suitable parameters, is equivalent to the original problem. The efficiency of several sparse inducing penalty functions have been fully analyzed. Four DCA (DC Algorithm) schemes were developed that cover all standard algorithms in nonconvex sparse approximation approaches as special versions. They can be viewed as, an ℓ1-perturbed algorithm/reweighted-ℓ1 algorithm / reweighted-ℓ2 algorithm. We offer a unifying nonconvex approximation approach, with solid theoretical tools as well as efficient algorithms based on DC programming and DCA, to tackle the zero-norm and sparse optimization. As an application, we implemented our methods for the feature selection in SVM (Support Vector Machine) problem and performed empirical comparative numerical experiments on the proposed algorithms with various approximation functions. Sparse optimization refers to an optimization problem involving the zero-norm in objective or constraints. In this paper, nonconvex approximation approaches for sparse optimization have been studied with a unifying point of view in DC (Difference of Convex functions) programming framework. Considering a common DC approximation of the zero-norm including all standard sparse inducing penalty functions, we studied the consistency between global minimums (resp. local minimums) of approximate and original problems. We showed that, in several cases, some global minimizers (resp. local minimizers) of the approximate problem are also those of the original problem. Using exact penalty techniques in DC programming, we proved stronger results for some particular approximations, namely, the approximate problem, with suitable parameters, is equivalent to the original problem. The efficiency of several sparse inducing penalty functions have been fully analyzed. Four DCA (DC Algorithm) schemes were developed that cover all standard algorithms in nonconvex sparse approximation approaches as special versions. They can be viewed as, an ...-perturbed algorithm/reweighted-... algorithm / reweighted-... algorithm. We offer a unifying nonconvex approximation approach, with solid theoretical tools as well as efficient algorithms based on DC programming and DCA, to tackle the zero-norm and sparse optimization. As an application, we implemented our methods for the feature selection in SVM (Support Vector Machine) problem and performed empirical comparative numerical experiments on the proposed algorithms with various approximation functions. (ProQuest: ... denotes formulae/symbols omitted.) Sparse optimization refers to an optimization problem involving the zero-norm in objective or constraints. In this paper, nonconvex approximation approaches for sparse optimization have been studied with a unifying point of view in DC (Difference of Convex functions) programming framework. Considering a common DC approximation of the zero-norm including all standard sparse inducing penalty functions, we studied the consistency between global minimums (resp. local minimums) of approximate and original problems. We showed that, in several cases, some global minimizers (resp. local minimizers) of the approximate problem are also those of the original problem. Using exact penalty techniques in DC programming, we proved stronger results for some particular approximations, namely, the approximate problem, with suitable parameters, is equivalent to the original problem. The efficiency of several sparse inducing penalty functions have been fully analyzed. Four DCA (DC Algorithm) schemes were developed that cover all standard algorithms in nonconvex sparse approximation approaches as special versions. They can be viewed as, an [ell] sub(1)-perturbed algorithm/reweighted-[ell] sub(1) algorithm / reweighted-[ell] sub(2) algorithm. We offer a unifying nonconvex approximation approach, with solid theoretical tools as well as efficient algorithms based on DC programming and DCA, to tackle the zero-norm and sparse optimization. As an application, we implemented our methods for the feature selection in SVM (Support Vector Machine) problem and performed empirical comparative numerical experiments on the proposed algorithms with various approximation functions. •A unifying DC approximation, including all standard approximations, of the zero-norm is proposed.•The consistency between global/local minima of approximate and original problems are proved.•The equivalence between approximate and original problems are established for some approximations.•Four DCA schemes are developed that cover all standard nonconvex approximation algorithms.•A careful empirical experiment for feature selection in SVM are performed. Sparse optimization refers to an optimization problem involving the zero-norm in objective or constraints. In this paper, nonconvex approximation approaches for sparse optimization have been studied with a unifying point of view in DC (Difference of Convex functions) programming framework. Considering a common DC approximation of the zero-norm including all standard sparse inducing penalty functions, we studied the consistency between global minimums (resp. local minimums) of approximate and original problems. We showed that, in several cases, some global minimizers (resp. local minimizers) of the approximate problem are also those of the original problem. Using exact penalty techniques in DC programming, we proved stronger results for some particular approximations, namely, the approximate problem, with suitable parameters, is equivalent to the original problem. The efficiency of several sparse inducing penalty functions have been fully analyzed. Four DCA (DC Algorithm) schemes were developed that cover all standard algorithms in nonconvex sparse approximation approaches as special versions. They can be viewed as, an ℓ1-perturbed algorithm/reweighted-ℓ1 algorithm / reweighted-ℓ2 algorithm. We offer a unifying nonconvex approximation approach, with solid theoretical tools as well as efficient algorithms based on DC programming and DCA, to tackle the zero-norm and sparse optimization. As an application, we implemented our methods for the feature selection in SVM (Support Vector Machine) problem and performed empirical comparative numerical experiments on the proposed algorithms with various approximation functions.
Author	Pham Dinh, T. Le Thi, H.A. Vo, X.T. Le, H.M.
Author_xml	– sequence: 1 givenname: H.A. surname: Le Thi fullname: Le Thi, H.A. email: hoai-an.le-thi@univ-lorraine.fr organization: Laboratory of Theoretical and Applied Computer Science EA 3097, University of Lorraine, Ile du Saulcy, Metz 57045, France – sequence: 2 givenname: T. surname: Pham Dinh fullname: Pham Dinh, T. email: pham@insa-rouen.fr organization: Laboratory of Mathematics, INSA – Rouen, University of Normandie, Saint-Etienne-du-Rouvray Cedex 76801, France – sequence: 3 givenname: H.M. surname: Le fullname: Le, H.M. email: minh.le@univ-lorraine.fr organization: Laboratory of Theoretical and Applied Computer Science EA 3097, University of Lorraine, Ile du Saulcy, Metz 57045, France – sequence: 4 givenname: X.T. surname: Vo fullname: Vo, X.T. email: xuan-thanh.vo@univ-lorraine.fr organization: Laboratory of Theoretical and Applied Computer Science EA 3097, University of Lorraine, Ile du Saulcy, Metz 57045, France
BackLink	https://hal.univ-lorraine.fr/hal-01616997$$DView record in HAL
BookMark	eNp9kE9LxDAQxYMouK5-AU8LXvTQmknaJAUvsv6FBS96DjGdYkq3qUlX1E9v1urFg6dhht8b3nsHZLf3PRJyDDQHCuK8zbH1IWcUihwgpxx2yAyUZJlQgu6SGeVSZoyB3CcHMbaUUiihnBF-tVyYYQj-3a3N6Hw_bca-YFw0PiziYELEhR9Gt3af38gh2WtMF_HoZ87J08314_IuWz3c3i8vV5ktBRuzRqCqCkP5sy1kLQpe2QZZXUhOeQmNEZyVaa8NU_UzQ1vJQiIvK6sqy0BwPidn098X0-khJIPhQ3vj9N3lSm9vKTmIqpJvkNjTiU3mXzcYR7120WLXmR79JmoQqlRMcaESevIHbf0m9ClJokRRyFIKmSg1UTb4GAM22rrxO_4YjOs0UL1tXrd627zeNq8BdGo-Sdkf6a_5f0UXkwhTo28Og47WYW-xdgHtqGvv_pN_ASIpnM4
CODEN	EJORDT
CitedBy_id	crossref_primary_10_1007_s10898_023_01272_1 crossref_primary_10_1016_j_neunet_2020_08_024 crossref_primary_10_1016_j_acha_2021_09_003 crossref_primary_10_1080_10556788_2021_1977809 crossref_primary_10_1007_s10107_018_01357_w crossref_primary_10_1109_TFUZZ_2019_2900859 crossref_primary_10_1007_s10479_023_05726_3 crossref_primary_10_1142_S021759592240005X crossref_primary_10_1016_j_knosys_2020_106707 crossref_primary_10_1162_NECO_a_00836 crossref_primary_10_1137_18M117337X crossref_primary_10_1137_20M1385706 crossref_primary_10_1287_moor_2020_0393 crossref_primary_10_1016_j_amc_2020_125904 crossref_primary_10_1109_TSP_2023_3262184 crossref_primary_10_1287_ijoc_2020_1004 crossref_primary_10_1007_s10489_016_0778_y crossref_primary_10_1287_ijoo_2024_0038 crossref_primary_10_1016_j_jtbi_2018_06_015 crossref_primary_10_1109_ACCESS_2019_2955108 crossref_primary_10_1049_iet_spr_2018_5130 crossref_primary_10_1007_s10915_025_02900_6 crossref_primary_10_1109_TSP_2018_2849746 crossref_primary_10_1007_s10107_021_01766_4 crossref_primary_10_1007_s10898_022_01220_5 crossref_primary_10_1016_j_jfranklin_2023_02_035 crossref_primary_10_1109_TIT_2024_3454694 crossref_primary_10_1109_JSEN_2017_2754502 crossref_primary_10_1007_s10589_024_00625_0 crossref_primary_10_1109_TSP_2016_2637314 crossref_primary_10_1007_s10107_018_1235_y crossref_primary_10_1007_s12190_022_01797_w crossref_primary_10_1137_21M142770X crossref_primary_10_1371_journal_pone_0315740 crossref_primary_10_1016_j_engappai_2016_04_003 crossref_primary_10_1007_s10915_024_02584_4 crossref_primary_10_1007_s10107_023_01966_0 crossref_primary_10_1080_10556788_2019_1684492 crossref_primary_10_1109_TSP_2023_3315449 crossref_primary_10_1007_s11042_023_17315_4 crossref_primary_10_1109_TSP_2023_3311523 crossref_primary_10_1007_s40305_025_00594_z crossref_primary_10_1016_j_neucom_2015_12_068 crossref_primary_10_1109_TSP_2023_3315454 crossref_primary_10_1016_j_ejor_2016_05_020 crossref_primary_10_1109_TCOMM_2019_2920594 crossref_primary_10_3233_JIFS_181501 crossref_primary_10_1016_j_cor_2016_11_003 crossref_primary_10_1109_LWC_2017_2680449 crossref_primary_10_1007_s11590_019_01408_x crossref_primary_10_1137_16M1084754 crossref_primary_10_1007_s10489_018_01407_y crossref_primary_10_1109_TCOMM_2021_3093331 crossref_primary_10_1109_TNNLS_2022_3213558 crossref_primary_10_1007_s00521_016_2216_9 crossref_primary_10_1016_j_jvcir_2021_103367 crossref_primary_10_1049_ipr2_12917 crossref_primary_10_1186_s13638_020_01804_3 crossref_primary_10_1007_s11081_017_9372_3 crossref_primary_10_1137_16M1059333 crossref_primary_10_1007_s10898_019_00857_z crossref_primary_10_1007_s10107_018_1236_x crossref_primary_10_1109_TIP_2019_2898843 crossref_primary_10_1016_j_apnum_2023_04_004 crossref_primary_10_1016_j_neucom_2025_129390 crossref_primary_10_1162_neco_a_01012 crossref_primary_10_1137_21M1443455 crossref_primary_10_1016_j_cmpb_2023_107503 crossref_primary_10_1007_s10107_024_02161_5 crossref_primary_10_1007_s10107_018_1286_0 crossref_primary_10_1016_j_ins_2020_07_068 crossref_primary_10_1016_j_neucom_2019_08_035 crossref_primary_10_1109_JSEN_2017_2730226 crossref_primary_10_1109_TNNLS_2022_3153310 crossref_primary_10_1007_s10472_021_09732_8 crossref_primary_10_1007_s11590_020_01696_8 crossref_primary_10_1016_j_neucom_2020_07_085 crossref_primary_10_1016_j_dsp_2019_02_009 crossref_primary_10_1007_s10898_019_00826_6 crossref_primary_10_1109_LSP_2024_3370493 crossref_primary_10_1109_TWC_2016_2578922 crossref_primary_10_1162_neco_a_01002 crossref_primary_10_1016_j_ejor_2024_07_019 crossref_primary_10_1137_18M1186009 crossref_primary_10_1007_s10915_024_02715_x crossref_primary_10_1007_s10589_022_00419_2 crossref_primary_10_1007_s11590_018_1280_8 crossref_primary_10_1137_22M1525363 crossref_primary_10_1007_s10479_016_2333_y crossref_primary_10_1016_j_compeleceng_2024_109306 crossref_primary_10_1051_itmconf_20213604007 crossref_primary_10_1109_ACCESS_2018_2879336 crossref_primary_10_1080_02331934_2021_1892675 crossref_primary_10_12677_AAM_2021_1011409 crossref_primary_10_1007_s10898_018_0698_y crossref_primary_10_1061_JSENDH_STENG_14658 crossref_primary_10_1016_j_neunet_2019_05_011 crossref_primary_10_1016_j_knosys_2020_106536 crossref_primary_10_1016_j_neucom_2020_12_045 crossref_primary_10_1016_j_bspc_2024_107327 crossref_primary_10_1007_s10107_018_1283_3 crossref_primary_10_1016_j_ejor_2025_06_004 crossref_primary_10_1016_j_neucom_2021_09_039 crossref_primary_10_1080_10556788_2025_2521527 crossref_primary_10_1016_j_dte_2025_100045 crossref_primary_10_1016_j_neunet_2022_03_033 crossref_primary_10_12677_AAM_2023_121022 crossref_primary_10_1016_j_neucom_2018_06_070 crossref_primary_10_1007_s00180_022_01249_w crossref_primary_10_1016_j_sigpro_2022_108754 crossref_primary_10_1287_moor_2021_0258 crossref_primary_10_1016_j_csda_2023_107902 crossref_primary_10_1080_00207543_2019_1657245 crossref_primary_10_1007_s10107_024_02103_1 crossref_primary_10_1007_s10898_021_01028_9 crossref_primary_10_1016_j_cor_2016_04_005 crossref_primary_10_1155_2021_3289477 crossref_primary_10_1007_s43670_025_00102_7 crossref_primary_10_1109_LSP_2023_3298283 crossref_primary_10_1109_TSP_2018_2871998 crossref_primary_10_1007_s10107_017_1181_0 crossref_primary_10_1007_s11081_017_9359_0 crossref_primary_10_1109_TWC_2020_2979147
Cites_doi	10.1007/s10994-005-1505-9 10.1007/s10589-008-9202-9 10.1016/j.csda.2013.01.020 10.1023/A:1008288411710 10.1198/016214505000000781 10.1007/s101070050003 10.1016/S0377-2217(01)00301-0 10.1023/A:1012487302797 10.1007/s11634-008-0030-7 10.1080/10618600.1998.10474784 10.1109/TIT.2005.858979 10.1109/TIT.2008.924688 10.1109/TSP.2002.808076 10.1023/A:1018361916442 10.1080/10556788.2011.652630 10.1007/s10957-012-0197-0 10.1007/s10618-014-0369-7 10.1007/s10898-009-9507-y 10.1109/TIT.2003.820031 10.1080/10556788.2011.645543 10.1016/j.neunet.2014.06.011 10.1198/016214506000000735 10.1214/009053607000000802 10.1093/bioinformatics/bti736 10.1016/j.sigpro.2007.08.015 10.1109/TSP.2009.2026004 10.1007/978-3-642-36543-0_40 10.1109/ACSSC.1993.342465 10.1007/s10898-011-9765-3 10.1137/S1052623494274313 10.1198/016214501753382273 10.1016/j.ejor.2005.07.028 10.1016/j.dam.2007.03.024 10.1137/S0097539792240406 10.1109/78.558475 10.1109/78.738251 10.1007/BF00994018 10.1007/s10898-012-9859-6 10.1109/78.258082 10.1007/s10479-004-5022-1 10.1007/s10589-010-9388-5 10.1109/TAC.2014.2301575 10.1137/090761471 10.1007/s11263-014-0784-7 10.1214/08-AOS659 10.1162/NECO_a_00490 10.1080/10556789208805504
ContentType	Journal Article
Copyright	2014 Elsevier B.V. Copyright Elsevier Sequoia S.A. Jul 1, 2015 Distributed under a Creative Commons Attribution 4.0 International License
Copyright_xml	– notice: 2014 Elsevier B.V. – notice: Copyright Elsevier Sequoia S.A. Jul 1, 2015 – notice: Distributed under a Creative Commons Attribution 4.0 International License
DBID	AAYXX CITATION 7SC 7TB 8FD FR3 JQ2 L7M L~C L~D 7TA JG9 1XC
DOI	10.1016/j.ejor.2014.11.031
DatabaseName	CrossRef Computer and Information Systems Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Materials Business File Materials Research Database Hyper Article en Ligne (HAL)
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional Materials Research Database Materials Business File
DatabaseTitleList	Technology Research Database Materials Research Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science Business
EISSN	1872-6860
EndPage	46
ExternalDocumentID	oai:HAL:hal-01616997v1 3628923491 10_1016_j_ejor_2014_11_031 S0377221714009540
Genre	Feature
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 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 AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR AXJTR BKOJK BKOMP BLXMC CS3 DU5 EBS EFJIC EFLBG EJD 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 RIG 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 AALRI 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 FEDTE FGOYB HVGLF HZ~ R2- SEW VH1 WUQ ~HD 7SC 7TB 8FD AFXIZ AGCQF AGRNS FR3 JQ2 L7M L~C L~D SSH 7TA JG9 1XC
ID	FETCH-LOGICAL-c562t-f6e894a03bc47d6439cfe2d4730351fa6325e2dda28db2ec9747e359c89c21633
ISICitedReferencesCount	164
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000352667900004&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 Oct 14 20:33:05 EDT 2025 Sun Nov 09 13:33:02 EST 2025 Fri Jul 25 04:22:10 EDT 2025 Tue Nov 18 22:14:39 EST 2025 Sat Nov 29 01:41:12 EST 2025 Fri Feb 23 02:27:39 EST 2024
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	Sparse optimization DC programming and DCA Global optimization DC approximation function Feature selection in SVM
Language	English
License	Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c562t-f6e894a03bc47d6439cfe2d4730351fa6325e2dda28db2ec9747e359c89c21633
Notes	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ORCID	0000-0001-7337-1505 0000-0002-2239-2100
PQID	1664475767
PQPubID	45678
PageCount	21
ParticipantIDs	hal_primary_oai_HAL_hal_01616997v1 proquest_miscellaneous_1685828368 proquest_journals_1664475767 crossref_citationtrail_10_1016_j_ejor_2014_11_031 crossref_primary_10_1016_j_ejor_2014_11_031 elsevier_sciencedirect_doi_10_1016_j_ejor_2014_11_031
PublicationCentury	2000
PublicationDate	2015-07-01
PublicationDateYYYYMMDD	2015-07-01
PublicationDate_xml	– month: 07 year: 2015 text: 2015-07-01 day: 01
PublicationDecade	2010
PublicationPlace	Amsterdam
PublicationPlace_xml	– name: Amsterdam
PublicationTitle	European journal of operational research
PublicationYear	2015
Publisher	Elsevier B.V Elsevier Sequoia S.A Elsevier
Publisher_xml	– name: Elsevier B.V – name: Elsevier Sequoia S.A – name: Elsevier
References	Guan, Gray (bib0022) 2013; 67 Zhang, Ahn, Lin, Park (bib0088) 2006; 2 Gorodnitsky, Rao (bib0019) 1997; 45 Le Thi, Moeini (bib0055) 2014; 161 Zhang (bib0087) 2009; 37 Sriperumbudur, Torres, Lanckriet (bib0078) 2007 Schmidt, Fung, Rosales (bib0077) 2007; 4701 Chan, Vasconcelos, Lanckriet (bib0010) 2007 Le, Le Thi, Nguyen (bib0047) 2013 Bradley, Mangasarian, Rosen (bib0005) 1998; 11 Le Thi, Pham Dinh (bib0050) 2013 Weston, Mukherjee, Chapelle, Pontil, Poggio, Vapnik (bib0085) 2001 Le Thi, Huynh, Pham Dinh (bib0042) 2009 Chartrand, Yin (bib0009) 2008 Le Thi (bib0030) 2000; 87 Cortes, Vapnik (bib0012) 1995; 20 Mohri, Medina (bib0061) 2014 Le Thi, Le, Pham Dinh (bib0054) 2014 Niu, Y. S., Pham Dinh, T., Le Thi, H. A. Judice, J. (2012). Efficient DC programming approaches for asymmetric eigenvalue complementarity problem, optimization methods and software, doi: 10.1080/10556788.2011.645543. Pham Dinh, Nguyen Canh, Le Thi (bib0070) 2010; 48 Le Thi, Nguyen, Le (bib0049) 2013 Le Thi, Huynh, Pham Dinh (bib0044) 2012; 52 Fawzi, Davies, Frossard (bib0015) 2014 Le, Le Thi, Pham Dinh, Huynh (bib0046) 2013; 25 Le Thi, Nguyen (bib0053) 2014; 28 Tan, Wang, Tsang (bib0080) 2010 Thiao, Pham Dinh, Le Thi (bib0082) 2010 Candes, Wakin, Boyd (bib0008) 2008; 14 Pham Dinh, Le, Le Thi, Lauer (bib0072) 2014; 59 Pham Dinh, Le Thi (bib0071) 2014; 8342 Zou (bib0089) 2006; 101 Gribonval, Nielsen (bib0018) 2003; 49 Guyon, Gunn, Nikravesh, Zadeh (bib0021) 2006 Candes, Randall (bib0007) 2006; 54 Tibshirani (bib0083) 1996; 46 Le Thi, Le, Nguyen, Pham Dinh (bib0040) 2008; 2 Le Thi, Pham Dinh (bib0039) 2008; 156 Pham Dinh, Le Thi (bib0069) 2002 Fan, Li (bib0014) 2001; 96 Le Thi, Pham Dinh, Nguyen Van (bib0032) 2002; 142 Takhar, Laska, Wakin, Duarte, Baron, Sarvotham (bib0079) 2006, January . Candes, Tao (bib0006) 2005; 51 Le Thi, H. A. (2012). DC programming and DCA. Le Thi, Pham Dinh (bib0034) 2005; 133 Natarajan (bib0062) 1995; 24 Heiler, Cremers, Schnörr (bib0024) 2001 Le Thi (bib0043) 2012 Le Thi, Moeini, Pham Dinh, Joaquim (bib0045) 2012; 51 Guyon, Weston, Barnhill, Vapnik (bib0020) 2002; 46 Le Thi (bib0027) 1994 Le Thi, Nguyen (bib0051) 2013 Hastie, Tibshirani, Friedman (bib0023) 2009 Gasso, Rakotomamonjy, Canu (bib0017) 2009; 57 Baron, Wakin, Duarte, Sarvotham, Baraniuk (bib0002) 2009, November Rinaldi, Schoen, Sciandrone (bib0075) 2010; 46 Le Thi, Vo, Pham Dinh (bib0052) 2014; 59 Trombettoni, Araya, Neveu, Chabert (bib0084) 2011 Rao, Engan, Cotter, Palmer, KreutzDelgado (bib0074) 2003; 51 Peleg, Meir (bib0026) 2008; 8 Le Thi, Pham Dinh (bib0029) 1997; 11 Neumann, Schnörr, Steidl (bib0063) 2005; 61 Bennett, Mangasarian (bib0003) 1992; 1 Le Thi, Nguyen, Ouchani (bib0041) 2009; 3 Le Thi, Nguyen, Pham Dinh (bib0037) 2007; 183 Le Thi, Huynh, Pham Dinh (bib0056) 2014; 282 Thiao, Pham Dinh, Le Thi (bib0081) 2008 Weston, Elisseeff, Scholkopf, Tipping (bib0086) 2003; 3 Le Thi, Le, Pham Dinh (bib0038) 2007; 183 Bradley, Mangasarian (bib0004) 1998 Pham Dinh, Le Thi (bib0068) 1998; 8 Collobert, Sinz, Weston, Bottou (bib0013) 2006 Le Thi (bib0028) 1997 Le Thi, Le, Pham Dinh (bib0036) 2006; 183 Rockafellar (bib0076) 1970 Chen, Xu, Ye (bib0011) 2010; 32 Pham Dinh, Le Thi (bib0067) 1997; 22 Ong, Le Thi (bib0065) 2013; 28 Le Thi, Le, Pham Dinh, Huynh (bib0048) 2013; 56 Rao, Kreutz-Delgado (bib0073) 1999; 47 Mallat, Zhang (bib0059) 1993; 41 Zou, Li (bib0090) 2008; 36 Liu, Zheng (bib0057) 2005 Le Thi, Pham Dinh (bib0033) 2003; 4 Bajwa, Haupt, Sayeed, Nowak (bib0001) 2006 Krause, Singer (bib0025) 2004 Le Thi, Belghiti, Pham Dinh (bib0035) 2006; 37 Mangasarian (bib0060) 1996 Fu (bib0016) 1998; 7 Liu, Shen (bib0058) 2006; 101 Pati, Rezaifar, Krishnaprasa (bib0066) 1993, November 10.1016/j.ejor.2014.11.031_bib0031 Le Thi (10.1016/j.ejor.2014.11.031_bib0041) 2009; 3 Guyon (10.1016/j.ejor.2014.11.031_bib0021) 2006 Takhar (10.1016/j.ejor.2014.11.031_bib0079) 2006 Chartrand (10.1016/j.ejor.2014.11.031_bib0009) 2008 Le Thi (10.1016/j.ejor.2014.11.031_bib0035) 2006; 37 Le Thi (10.1016/j.ejor.2014.11.031_bib0037) 2007; 183 Le Thi (10.1016/j.ejor.2014.11.031_bib0043) 2012 Le Thi (10.1016/j.ejor.2014.11.031_bib0039) 2008; 156 Le Thi (10.1016/j.ejor.2014.11.031_bib0036) 2006; 183 Bajwa (10.1016/j.ejor.2014.11.031_bib0001) 2006 Pham Dinh (10.1016/j.ejor.2014.11.031_bib0069) 2002 Liu (10.1016/j.ejor.2014.11.031_bib0058) 2006; 101 Le Thi (10.1016/j.ejor.2014.11.031_bib0052) 2014; 59 Le Thi (10.1016/j.ejor.2014.11.031_bib0053) 2014; 28 Le Thi (10.1016/j.ejor.2014.11.031_bib0033) 2003; 4 Le Thi (10.1016/j.ejor.2014.11.031_bib0045) 2012; 51 Le Thi (10.1016/j.ejor.2014.11.031_bib0050) 2013 Le Thi (10.1016/j.ejor.2014.11.031_bib0055) 2014; 161 Le Thi (10.1016/j.ejor.2014.11.031_bib0029) 1997; 11 Krause (10.1016/j.ejor.2014.11.031_bib0025) 2004 Le Thi (10.1016/j.ejor.2014.11.031_bib0048) 2013; 56 Mangasarian (10.1016/j.ejor.2014.11.031_bib0060) 1996 Le Thi (10.1016/j.ejor.2014.11.031_bib0034) 2005; 133 Pham Dinh (10.1016/j.ejor.2014.11.031_bib0070) 2010; 48 Zhang (10.1016/j.ejor.2014.11.031_bib0088) 2006; 2 Pham Dinh (10.1016/j.ejor.2014.11.031_bib0068) 1998; 8 Le Thi (10.1016/j.ejor.2014.11.031_bib0032) 2002; 142 Gribonval (10.1016/j.ejor.2014.11.031_bib0018) 2003; 49 Neumann (10.1016/j.ejor.2014.11.031_bib0063) 2005; 61 Pham Dinh (10.1016/j.ejor.2014.11.031_bib0071) 2014; 8342 Le Thi (10.1016/j.ejor.2014.11.031_bib0027) 1994 Le Thi (10.1016/j.ejor.2014.11.031_bib0030) 2000; 87 Tan (10.1016/j.ejor.2014.11.031_bib0080) 2010 Candes (10.1016/j.ejor.2014.11.031_bib0008) 2008; 14 Weston (10.1016/j.ejor.2014.11.031_bib0086) 2003; 3 Bradley (10.1016/j.ejor.2014.11.031_bib0004) 1998 Peleg (10.1016/j.ejor.2014.11.031_bib0026) 2008; 8 Schmidt (10.1016/j.ejor.2014.11.031_bib0077) 2007; 4701 Guyon (10.1016/j.ejor.2014.11.031_bib0020) 2002; 46 Candes (10.1016/j.ejor.2014.11.031_bib0006) 2005; 51 Gasso (10.1016/j.ejor.2014.11.031_bib0017) 2009; 57 Le Thi (10.1016/j.ejor.2014.11.031_bib0051) 2013 Le Thi (10.1016/j.ejor.2014.11.031_bib0056) 2014; 282 Pham Dinh (10.1016/j.ejor.2014.11.031_bib0067) 1997; 22 Le Thi (10.1016/j.ejor.2014.11.031_bib0054) 2014 Thiao (10.1016/j.ejor.2014.11.031_bib0081) 2008 Le Thi (10.1016/j.ejor.2014.11.031_bib0049) 2013 Pham Dinh (10.1016/j.ejor.2014.11.031_bib0072) 2014; 59 Tibshirani (10.1016/j.ejor.2014.11.031_bib0083) 1996; 46 Zhang (10.1016/j.ejor.2014.11.031_bib0087) 2009; 37 Rao (10.1016/j.ejor.2014.11.031_bib0073) 1999; 47 Trombettoni (10.1016/j.ejor.2014.11.031_bib0084) 2011 Mallat (10.1016/j.ejor.2014.11.031_bib0059) 1993; 41 Rockafellar (10.1016/j.ejor.2014.11.031_bib0076) 1970 Fan (10.1016/j.ejor.2014.11.031_bib0014) 2001; 96 Heiler (10.1016/j.ejor.2014.11.031_sbref0024) 2001 Le Thi (10.1016/j.ejor.2014.11.031_bib0040) 2008; 2 Baron (10.1016/j.ejor.2014.11.031_bib0002) 2009 Chen (10.1016/j.ejor.2014.11.031_bib0011) 2010; 32 Liu (10.1016/j.ejor.2014.11.031_bib0057) 2005 Mohri (10.1016/j.ejor.2014.11.031_bib0061) 2014 Ong (10.1016/j.ejor.2014.11.031_bib0065) 2013; 28 Natarajan (10.1016/j.ejor.2014.11.031_bib0062) 1995; 24 Le Thi (10.1016/j.ejor.2014.11.031_bib0038) 2007; 183 Le (10.1016/j.ejor.2014.11.031_bib0046) 2013; 25 Guan (10.1016/j.ejor.2014.11.031_bib0022) 2013; 67 Pati (10.1016/j.ejor.2014.11.031_bib0066) 1993 Candes (10.1016/j.ejor.2014.11.031_bib0007) 2006; 54 Fawzi (10.1016/j.ejor.2014.11.031_bib0015) 2014 10.1016/j.ejor.2014.11.031_bib0064 Bradley (10.1016/j.ejor.2014.11.031_bib0005) 1998; 11 Sriperumbudur (10.1016/j.ejor.2014.11.031_bib0078) 2007 Weston (10.1016/j.ejor.2014.11.031_bib0085) 2001 Rao (10.1016/j.ejor.2014.11.031_bib0074) 2003; 51 Le Thi (10.1016/j.ejor.2014.11.031_bib0042) 2009 Le (10.1016/j.ejor.2014.11.031_bib0047) 2013 Rinaldi (10.1016/j.ejor.2014.11.031_bib0075) 2010; 46 Cortes (10.1016/j.ejor.2014.11.031_bib0012) 1995; 20 Gorodnitsky (10.1016/j.ejor.2014.11.031_bib0019) 1997; 45 Hastie (10.1016/j.ejor.2014.11.031_bib0023) 2009 Le Thi (10.1016/j.ejor.2014.11.031_bib0044) 2012; 52 Chan (10.1016/j.ejor.2014.11.031_bib0010) 2007 Bennett (10.1016/j.ejor.2014.11.031_bib0003) 1992; 1 Zou (10.1016/j.ejor.2014.11.031_bib0090) 2008; 36 Collobert (10.1016/j.ejor.2014.11.031_bib0013) 2006 Fu (10.1016/j.ejor.2014.11.031_bib0016) 1998; 7 Thiao (10.1016/j.ejor.2014.11.031_bib0082) 2010 Le Thi (10.1016/j.ejor.2014.11.031_bib0028) 1997 Zou (10.1016/j.ejor.2014.11.031_bib0089) 2006; 101
References_xml	– volume: 8342 start-page: 1 year: 2014 end-page: 37 ident: bib0071 article-title: Recent advances in DC programming and DCA publication-title: Transactions on Computational Collective Intelligence – year: 1970 ident: bib0076 publication-title: Convex analysis – volume: 22 start-page: 289 year: 1997 end-page: 355 ident: bib0067 article-title: Convex analysis approach to d.c. programming: Theory, algorithm and applications publication-title: Acta Mathematica Vietnamica – reference: Niu, Y. S., Pham Dinh, T., Le Thi, H. A. Judice, J. (2012). Efficient DC programming approaches for asymmetric eigenvalue complementarity problem, optimization methods and software, doi: 10.1080/10556788.2011.645543. – year: 1994 ident: bib0027 publication-title: Analyse numérique des algorithmes de l’Optimisation d. c. Approches locales et globales. Codes et simulations numériques en grande dimension. Applications – start-page: 145 year: 2007 end-page: 153 ident: bib0010 article-title: Direct convex relaxations of sparse SVM publication-title: Proceeding ICML’07. Proceedings of the 24th international conference on machine learning – volume: 8 start-page: 375 year: 2008 end-page: 389 ident: bib0026 article-title: A bilinear formulation for vector sparsity optimization publication-title: Signal Processing – volume: 59 start-page: 2277 year: 2014 end-page: 2282 ident: bib0072 article-title: A DC programming algorithm for switched linear regression publication-title: IEEE Transactions on Automatic Control – year: 2009, November ident: bib0002 publication-title: Distributed compressed sensing – start-page: 134 year: 2006 end-page: 142 ident: bib0001 article-title: Compressive wireless sensing publication-title: Proceedings of fifth international conference on information processing in sensor networks – volume: 8 start-page: 476 year: 1998 end-page: 505 ident: bib0068 article-title: DC optimization algorithms for solving the trust region subproblem. publication-title: SIAM Journal of Optimization – volume: 56 start-page: 1393 year: 2013 end-page: 1407 ident: bib0048 article-title: Binary classification via spherical separator by DC programming and DCA publication-title: Journal of Global Optimization – volume: 7 start-page: 397 year: 1998 end-page: 416 ident: bib0016 article-title: Penalized regression: The bridge versus the lasso publication-title: Journal of Computational and Graphical Statistics – volume: 161 start-page: 199 year: 2014 end-page: 224 ident: bib0055 article-title: Long-short portfolio optimization under cardinality constraints by difference of convex functions algorithm publication-title: Journal of Optimization Theory, & Applications – year: 2001 ident: bib0085 article-title: Feature selection for SVMs publication-title: Neural information processing systems – year: 2006 ident: bib0013 article-title: Trading convexity for scalability publication-title: Proceedings of the 23th international conference on machine learning (ICML 2006) – year: 1997 ident: bib0028 publication-title: Contribution à l’optimisation non convexe et l’optimisation globale: Théorie, algorithmes et applications – volume: 4701 start-page: 286 year: 2007 end-page: 297 ident: bib0077 article-title: Fast optimization methods for L1 regularization: A comparative study and two new approaches publication-title: ECML 2007, Lecture Notes in Computer Science – volume: 37 start-page: 2109 year: 2009 end-page: 2144 ident: bib0087 article-title: Some sharp performance bounds for least squares regression with regularization publication-title: Annals of Statistics – volume: 32 start-page: 2832 year: 2010 end-page: 2852 ident: bib0011 article-title: Lower bound theory of nonzero entries in solutions of l2-lp minimization publication-title: SIAM Journal on Scientific Computing – year: 2002 ident: bib0069 article-title: DC programming: Theory, algorithms and applications. The state of the art publication-title: Proceedings of the first international workshop on global constrained optimization and constraint satisfaction (Cocos’ 02) – volume: 2 start-page: 88 year: 2006 end-page: 95 ident: bib0088 article-title: Gene selection using support vector machines with non-convex penalty publication-title: Bioinformatics – volume: 57 start-page: 4686 year: 2009 end-page: 4698 ident: bib0017 article-title: Recovering sparse signals with a certain family of nonconvex penalties and dc programming publication-title: IEEE Transactions on Signal Processing – volume: 183 start-page: 1067 year: 2007 end-page: 1085 ident: bib0038 article-title: Optimization based DC programming and DCA for hierarchical clustering publication-title: European Journal of Operational Research – volume: 14 start-page: 877 year: 2008 end-page: 905 ident: bib0008 article-title: Enhancing sparsity by reweighted- publication-title: Journal of Mathematical Analysis and Applications – volume: 37 start-page: 593 year: 2006 end-page: 608 ident: bib0035 article-title: A new efficient algorithm based on DC programming and DCA for clustering publication-title: Journal of Global Optimization – year: 1993, November ident: bib0066 article-title: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition publication-title: 27th asilomar conference on signals, systems and computers – volume: 41 start-page: 3397 year: 1993 end-page: 3415 ident: bib0059 article-title: Matching pursuit in a time-frequency dictionary publication-title: IEEE Transactions on Signal Processing – volume: 51 start-page: 1097 year: 2012 end-page: 1117 ident: bib0045 article-title: A DC programming approach for solving the symmetric eigenvalue complementarity problem publication-title: Computational Optimization and Applications – volume: 25 start-page: 2776 year: 2013 end-page: 2807 ident: bib0046 article-title: Block clustering based on difference of convex functions (DC) programming and DC algorithms publication-title: Neural Computation – volume: 142 start-page: 258 year: 2002 end-page: 270 ident: bib0032 article-title: Combination between local and global methods for solving an optimization problem over the efficient set publication-title: European Journal of Operational Research – year: 2010 ident: bib0080 article-title: Learning sparse svm for feature selection on very high dimensional datasets. publication-title: ICML 2010 – start-page: 175 year: 1996 end-page: 188 ident: bib0060 article-title: Machine learning via polyhedral concave minimization publication-title: Applied mathematics and parallel computing – Festschrift for Klaus Ritter – year: 2012 ident: bib0043 publication-title: A new approximation for the ℓ – year: 2014 ident: bib0061 article-title: Learning theory and algorithms for revenue optimization in second-price auctions with reserve publication-title: Proceeding ICML’14. Proceedings of the 31th international conference on machine learning – volume: 28 start-page: 830 year: 2013 end-page: 854 ident: bib0065 article-title: Learning sparse classifiers with difference of convex functions algorithms publication-title: Optimization Methods and Software – volume: 46 start-page: 431 year: 1996 end-page: 439 ident: bib0083 article-title: Regression shrinkage and selection via the lasso publication-title: Journal of The Royal Statistical Society – start-page: 348 year: 2008 end-page: 357 ident: bib0081 article-title: DC programming approach for solving a class of nonconvex programs dealing with zero-norm publication-title: Modelling, computation and optimization in information systems and management science, CCIS 14 – volume: 61 start-page: 129 year: 2005 end-page: 150 ident: bib0063 article-title: Combined SVM-based feature selection and classification. publication-title: Machine Learning – volume: 20 start-page: 273 year: 1995 end-page: 297 ident: bib0012 article-title: Support vector networks publication-title: Machine Learning – volume: 47 start-page: 87 year: 1999 end-page: 200 ident: bib0073 article-title: An affine scaling methodology for best basis selection publication-title: IEEE Transactions on Signal Processing – year: 2001 ident: bib0024 publication-title: Efficient feature subset selection for support vector machines – volume: 48 start-page: 595 year: 2010 end-page: 632 ident: bib0070 article-title: An efficient combination of DCA and B&B using DC/SDP relaxation for globally solving binary quadratic programs publication-title: Journal of Global Optimization – year: 2006 ident: bib0021 publication-title: Feature extraction, foundations and applications – volume: 3 start-page: 62 year: 2009 ident: bib0041 article-title: Gene selection for cancer classification using DCA publication-title: Journal of Fonctiers of Computer Science and Technology – volume: 24 start-page: 227 year: 1995 end-page: 234 ident: bib0062 article-title: Sparse approximate solutions to linear systems publication-title: SIAM Journal on Scientific Computing – volume: 11 start-page: 5 year: 1998 end-page: 21 ident: bib0005 article-title: Parsimonious least norm approximation publication-title: Computational Optimization and Applications – volume: 87 start-page: 401 year: 2000 end-page: 426 ident: bib0030 article-title: An efficient algorithm for globally minimizing a quadratic function under convex quadratic constraints publication-title: Mathematical Programming – volume: 96 start-page: 1348 year: 2001 end-page: 1360 ident: bib0014 article-title: Variable selection via nonconcave penalized likelihood and its oracle properties publication-title: Journal of the American Statistical Association – year: 2006, January ident: bib0079 article-title: A new compressive imaging camera architecture using optical-domain compression publication-title: Computational imaging IV at IS&T/SPIE electronic imaging – volume: 54 start-page: 2829 year: 2006 end-page: 2840 ident: bib0007 article-title: Highly robust error correction by convex programming publication-title: IEEE Transactions on Information Theory – volume: 45 start-page: 600 year: 1997 end-page: 616 ident: bib0019 article-title: Sparse signal reconstructions from limited data using FOCUSS: A re-weighted minimum norm algorithm publication-title: IEEE Transactions on Signal Processing – volume: 133 start-page: 23 year: 2005 end-page: 46 ident: bib0034 article-title: The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems publication-title: Annals of Operations Research – year: 2009 ident: bib0042 publication-title: Convergence analysis of DC algorithms for DC programming with subanalytic data – start-page: 831 year: 2007 end-page: 838 ident: bib0078 article-title: Sparse eigen methods by D.C. programming publication-title: Proceeding ICML ’07. Proceedings of the 24th international conference on machine learning – volume: 1 start-page: 23 year: 1992 end-page: 34 ident: bib0003 article-title: Robust linear programming discrimination of two linearly inseparable sets publication-title: Optimization Methods and Software – volume: 46 start-page: 467 year: 2010 end-page: 486 ident: bib0075 article-title: Concave programming for minimizing the zero-norm over polyhedral sets publication-title: Computational Optimization and Applications – volume: 67 start-page: 136 year: 2013 end-page: 148 ident: bib0022 article-title: Sparse high-dimensional fractional-norm support vector machine via DC programming publication-title: Computational Statistics and Data Analysis – year: 2014 ident: bib0054 article-title: Feature selection in machine learning: An exact penalty approach using a difference of convex function algorithm publication-title: Machine Learning – year: 2008 ident: bib0009 article-title: Iteratively reweighted algorithms for compressive sensing publication-title: ICASSP – volume: 156 start-page: 325 year: 2008 end-page: 338 ident: bib0039 article-title: A continuous approach for the concave cost supply problem via DC programming and DCA publication-title: Discrete Applied Mathematics – year: 2009 ident: bib0023 publication-title: The elements of statistical learning – start-page: 387 year: 2013 end-page: 397 ident: bib0049 article-title: Sparse signal recovery by difference of convex functions algorithms publication-title: Lecture notes in computer science – volume: 183 start-page: 1001 year: 2007 end-page: 1012 ident: bib0037 article-title: A continuous approach for solving the concave cost supply problem by combining DCA and B&B techniques publication-title: European Journal of Operational Research – volume: 101 start-page: 1418 year: 2006 end-page: 1429 ident: bib0089 article-title: The adaptive lasso and its oracle properties publication-title: Journal of the American Statistical Association – year: 2005 ident: bib0057 article-title: FS SFS: A novel feature selection method for support vector machines publication-title: Pattern Recognition – year: 2004 ident: bib0025 article-title: Leveraging the margin more carefully publication-title: Proceedings of the 21st international conference on machine learning, ICML 2004 – year: 1998 ident: bib0004 article-title: Feature Selection via concave minimization and support vector machines publication-title: Proceeding of international conference on machine learning ICML’98 – volume: 49 start-page: 3320 year: 2003 end-page: 3325 ident: bib0018 article-title: Sparse representation in union of bases publication-title: IEEE Transactions on Information Theory – year: 2013 ident: bib0051 article-title: Efficient algorithms for feature selection in multi-class support vector machine publication-title: Advanced computational methods for knowledge engineering. Studies in computational intelligence 479 – reference: Le Thi, H. A. (2012). DC programming and DCA. – volume: 2 start-page: 259 year: 2008 end-page: 278 ident: bib0040 article-title: A dc programming approach for feature selection in support vector machines learning publication-title: Journal of Advances in Data Analysis and Classification – volume: 36 start-page: 1509 year: 2008 end-page: 1533 ident: bib0090 article-title: One-step sparse estimates in nonconcave penalized likelihood models publication-title: Annals of Statistics – volume: 28 start-page: 1336 year: 2014 end-page: 1365 ident: bib0053 article-title: Self-organizing maps by difference of convex functions optimization publication-title: Data Mining and Knowledge Discovery – volume: 4 start-page: 77 year: 2003 end-page: 116 ident: bib0033 article-title: Large scale molecular optimization from distance matrices by a D.C. optimization approach publication-title: SIAM Journal on Optimization – year: 2014 ident: bib0015 article-title: Dictionary learning for fast classification based on soft-thresholding publication-title: International Journal of Computer Vision – start-page: 528 year: 2013 end-page: 542 ident: bib0047 article-title: DCA based algorithms for feature selection in semi-supervised support vector machines publication-title: Machine learning and data mining in pattern recognition – volume: 3 start-page: 1439 year: 2003 end-page: 1461 ident: bib0086 article-title: Use of the zero-norm with linear models and kernel methods publication-title: Journal of Machine Learning Research – reference: . – volume: 52 start-page: 509 year: 2012 end-page: 535 ident: bib0044 article-title: Exact penalty and error bounds in DC programming publication-title: Journal of Global Optimization – volume: 11 start-page: 253 year: 1997 end-page: 285 ident: bib0029 article-title: Solving a class of linearly constrained indefinite quadratic problems by DC algorithms publication-title: Journal of Global Optimization – start-page: 1063 year: 2010 end-page: 1070 ident: bib0082 article-title: A DC programming approach for sparse eigenvalue problem. publication-title: Proceedings of the 27th international conference on machine learning, ICML 2010 – volume: 183 start-page: 1067 year: 2006 end-page: 1085 ident: bib0036 article-title: Optimization based DC programming and DCA for hierarchical clustering publication-title: European Journal of Operational Research – volume: 59 start-page: 36 year: 2014 end-page: 50 ident: bib0052 article-title: Feature selection for linear SVMs under uncertain data: Robust optimization based on difference of convex functions algorithms. publication-title: Neural Networks – start-page: 99 year: 2011 end-page: 104 ident: bib0084 article-title: Inner regions and interval linearizations for global optimization publication-title: Proceedings of the twenty-fifth AAAI conference on artificial intelligence – start-page: 225 year: 2013 end-page: 290 ident: bib0050 article-title: DC programming approaches for distance geometry problems publication-title: Distance geometry: Theory, methods and applications – volume: 46 start-page: 389 year: 2002 end-page: 422 ident: bib0020 article-title: Gene selection for cancer classification using support vector machines publication-title: Machine Learning – volume: 282 start-page: 15 year: 2014 end-page: 35 ident: bib0056 article-title: DC programming and DCA for solving general DC programs publication-title: Proceedings of 2nd international conference on computer science, applied mathematics and applications (ICCSAMA 2014), Advances in Intelligent systems and Computing – volume: 101 start-page: 500 year: 2006 end-page: 509 ident: bib0058 article-title: Multicategory publication-title: Journal of the American Statistical Association – volume: 51 start-page: 4203 year: 2005 end-page: 4215 ident: bib0006 article-title: Decoding by linear programming publication-title: IEEE Transactions on Information Theory – volume: 51 start-page: 760 year: 2003 end-page: 770 ident: bib0074 article-title: Subset selection in noise based on diversity measure minimization publication-title: IEEE Transactions on Signal Processing – volume: 61 start-page: 129 issue: 1–3 year: 2005 ident: 10.1016/j.ejor.2014.11.031_bib0063 article-title: Combined SVM-based feature selection and classification. publication-title: Machine Learning doi: 10.1007/s10994-005-1505-9 – volume: 46 start-page: 467 issue: 3 year: 2010 ident: 10.1016/j.ejor.2014.11.031_bib0075 article-title: Concave programming for minimizing the zero-norm over polyhedral sets publication-title: Computational Optimization and Applications doi: 10.1007/s10589-008-9202-9 – volume: 67 start-page: 136 year: 2013 ident: 10.1016/j.ejor.2014.11.031_bib0022 article-title: Sparse high-dimensional fractional-norm support vector machine via DC programming publication-title: Computational Statistics and Data Analysis doi: 10.1016/j.csda.2013.01.020 – year: 1970 ident: 10.1016/j.ejor.2014.11.031_bib0076 – volume: 11 start-page: 253 issue: 3 year: 1997 ident: 10.1016/j.ejor.2014.11.031_bib0029 article-title: Solving a class of linearly constrained indefinite quadratic problems by DC algorithms publication-title: Journal of Global Optimization doi: 10.1023/A:1008288411710 – volume: 101 start-page: 500 year: 2006 ident: 10.1016/j.ejor.2014.11.031_bib0058 article-title: Multicategory ψ-learning publication-title: Journal of the American Statistical Association doi: 10.1198/016214505000000781 – volume: 87 start-page: 401 issue: 3 year: 2000 ident: 10.1016/j.ejor.2014.11.031_bib0030 article-title: An efficient algorithm for globally minimizing a quadratic function under convex quadratic constraints publication-title: Mathematical Programming doi: 10.1007/s101070050003 – year: 2005 ident: 10.1016/j.ejor.2014.11.031_bib0057 article-title: FS SFS: A novel feature selection method for support vector machines publication-title: Pattern Recognition – year: 2013 ident: 10.1016/j.ejor.2014.11.031_bib0051 article-title: Efficient algorithms for feature selection in multi-class support vector machine – volume: 142 start-page: 258 year: 2002 ident: 10.1016/j.ejor.2014.11.031_bib0032 article-title: Combination between local and global methods for solving an optimization problem over the efficient set publication-title: European Journal of Operational Research doi: 10.1016/S0377-2217(01)00301-0 – volume: 46 start-page: 389 issue: 1–3 year: 2002 ident: 10.1016/j.ejor.2014.11.031_bib0020 article-title: Gene selection for cancer classification using support vector machines publication-title: Machine Learning doi: 10.1023/A:1012487302797 – volume: 2 start-page: 259 year: 2008 ident: 10.1016/j.ejor.2014.11.031_bib0040 article-title: A dc programming approach for feature selection in support vector machines learning publication-title: Journal of Advances in Data Analysis and Classification doi: 10.1007/s11634-008-0030-7 – year: 2001 ident: 10.1016/j.ejor.2014.11.031_sbref0024 – year: 2014 ident: 10.1016/j.ejor.2014.11.031_bib0054 article-title: Feature selection in machine learning: An exact penalty approach using a difference of convex function algorithm – volume: 7 start-page: 397 year: 1998 ident: 10.1016/j.ejor.2014.11.031_bib0016 article-title: Penalized regression: The bridge versus the lasso publication-title: Journal of Computational and Graphical Statistics doi: 10.1080/10618600.1998.10474784 – volume: 51 start-page: 4203 issue: 12 year: 2005 ident: 10.1016/j.ejor.2014.11.031_bib0006 article-title: Decoding by linear programming publication-title: IEEE Transactions on Information Theory doi: 10.1109/TIT.2005.858979 – volume: 54 start-page: 2829 year: 2006 ident: 10.1016/j.ejor.2014.11.031_bib0007 article-title: Highly robust error correction by convex programming publication-title: IEEE Transactions on Information Theory doi: 10.1109/TIT.2008.924688 – volume: 51 start-page: 760 issue: 3 year: 2003 ident: 10.1016/j.ejor.2014.11.031_bib0074 article-title: Subset selection in noise based on diversity measure minimization publication-title: IEEE Transactions on Signal Processing doi: 10.1109/TSP.2002.808076 – volume: 11 start-page: 5 issue: 1 year: 1998 ident: 10.1016/j.ejor.2014.11.031_bib0005 article-title: Parsimonious least norm approximation publication-title: Computational Optimization and Applications doi: 10.1023/A:1018361916442 – year: 2001 ident: 10.1016/j.ejor.2014.11.031_bib0085 article-title: Feature selection for SVMs – volume: 28 start-page: 830 issue: 4 year: 2013 ident: 10.1016/j.ejor.2014.11.031_bib0065 article-title: Learning sparse classifiers with difference of convex functions algorithms publication-title: Optimization Methods and Software doi: 10.1080/10556788.2011.652630 – volume: 4701 start-page: 286 year: 2007 ident: 10.1016/j.ejor.2014.11.031_bib0077 article-title: Fast optimization methods for L1 regularization: A comparative study and two new approaches – volume: 161 start-page: 199 issue: 1 year: 2014 ident: 10.1016/j.ejor.2014.11.031_bib0055 article-title: Long-short portfolio optimization under cardinality constraints by difference of convex functions algorithm publication-title: Journal of Optimization Theory, & Applications doi: 10.1007/s10957-012-0197-0 – year: 2009 ident: 10.1016/j.ejor.2014.11.031_bib0042 – start-page: 99 year: 2011 ident: 10.1016/j.ejor.2014.11.031_bib0084 article-title: Inner regions and interval linearizations for global optimization – year: 1998 ident: 10.1016/j.ejor.2014.11.031_bib0004 article-title: Feature Selection via concave minimization and support vector machines – volume: 37 start-page: 593 year: 2006 ident: 10.1016/j.ejor.2014.11.031_bib0035 article-title: A new efficient algorithm based on DC programming and DCA for clustering publication-title: Journal of Global Optimization – start-page: 134 year: 2006 ident: 10.1016/j.ejor.2014.11.031_bib0001 article-title: Compressive wireless sensing – year: 2006 ident: 10.1016/j.ejor.2014.11.031_bib0013 article-title: Trading convexity for scalability – volume: 183 start-page: 1067 year: 2006 ident: 10.1016/j.ejor.2014.11.031_bib0036 article-title: Optimization based DC programming and DCA for hierarchical clustering publication-title: European Journal of Operational Research – volume: 28 start-page: 1336 issue: 5–6 year: 2014 ident: 10.1016/j.ejor.2014.11.031_bib0053 article-title: Self-organizing maps by difference of convex functions optimization publication-title: Data Mining and Knowledge Discovery doi: 10.1007/s10618-014-0369-7 – volume: 22 start-page: 289 year: 1997 ident: 10.1016/j.ejor.2014.11.031_bib0067 article-title: Convex analysis approach to d.c. programming: Theory, algorithm and applications publication-title: Acta Mathematica Vietnamica – year: 2014 ident: 10.1016/j.ejor.2014.11.031_bib0061 article-title: Learning theory and algorithms for revenue optimization in second-price auctions with reserve – start-page: 225 year: 2013 ident: 10.1016/j.ejor.2014.11.031_bib0050 article-title: DC programming approaches for distance geometry problems – volume: 48 start-page: 595 issue: 4 year: 2010 ident: 10.1016/j.ejor.2014.11.031_bib0070 article-title: An efficient combination of DCA and B&B using DC/SDP relaxation for globally solving binary quadratic programs publication-title: Journal of Global Optimization doi: 10.1007/s10898-009-9507-y – year: 2009 ident: 10.1016/j.ejor.2014.11.031_bib0002 – volume: 49 start-page: 3320 year: 2003 ident: 10.1016/j.ejor.2014.11.031_bib0018 article-title: Sparse representation in union of bases publication-title: IEEE Transactions on Information Theory doi: 10.1109/TIT.2003.820031 – ident: 10.1016/j.ejor.2014.11.031_bib0064 doi: 10.1080/10556788.2011.645543 – volume: 59 start-page: 36 year: 2014 ident: 10.1016/j.ejor.2014.11.031_bib0052 article-title: Feature selection for linear SVMs under uncertain data: Robust optimization based on difference of convex functions algorithms. publication-title: Neural Networks doi: 10.1016/j.neunet.2014.06.011 – volume: 101 start-page: 1418 year: 2006 ident: 10.1016/j.ejor.2014.11.031_bib0089 article-title: The adaptive lasso and its oracle properties publication-title: Journal of the American Statistical Association doi: 10.1198/016214506000000735 – volume: 4 start-page: 77 issue: 1 year: 2003 ident: 10.1016/j.ejor.2014.11.031_bib0033 article-title: Large scale molecular optimization from distance matrices by a D.C. optimization approach publication-title: SIAM Journal on Optimization – volume: 282 start-page: 15 year: 2014 ident: 10.1016/j.ejor.2014.11.031_bib0056 article-title: DC programming and DCA for solving general DC programs – volume: 46 start-page: 431 year: 1996 ident: 10.1016/j.ejor.2014.11.031_bib0083 article-title: Regression shrinkage and selection via the lasso publication-title: Journal of The Royal Statistical Society – volume: 36 start-page: 1509 issue: 4 year: 2008 ident: 10.1016/j.ejor.2014.11.031_bib0090 article-title: One-step sparse estimates in nonconcave penalized likelihood models publication-title: Annals of Statistics doi: 10.1214/009053607000000802 – volume: 14 start-page: 877 year: 2008 ident: 10.1016/j.ejor.2014.11.031_bib0008 article-title: Enhancing sparsity by reweighted-l1 minimization publication-title: Journal of Mathematical Analysis and Applications – year: 2009 ident: 10.1016/j.ejor.2014.11.031_bib0023 – volume: 2 start-page: 88 issue: 1 year: 2006 ident: 10.1016/j.ejor.2014.11.031_bib0088 article-title: Gene selection using support vector machines with non-convex penalty publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti736 – volume: 8 start-page: 375 issue: 2 year: 2008 ident: 10.1016/j.ejor.2014.11.031_bib0026 article-title: A bilinear formulation for vector sparsity optimization publication-title: Signal Processing doi: 10.1016/j.sigpro.2007.08.015 – volume: 57 start-page: 4686 year: 2009 ident: 10.1016/j.ejor.2014.11.031_bib0017 article-title: Recovering sparse signals with a certain family of nonconvex penalties and dc programming publication-title: IEEE Transactions on Signal Processing doi: 10.1109/TSP.2009.2026004 – volume: 3 start-page: 1439 year: 2003 ident: 10.1016/j.ejor.2014.11.031_bib0086 article-title: Use of the zero-norm with linear models and kernel methods publication-title: Journal of Machine Learning Research – start-page: 387 year: 2013 ident: 10.1016/j.ejor.2014.11.031_bib0049 article-title: Sparse signal recovery by difference of convex functions algorithms doi: 10.1007/978-3-642-36543-0_40 – year: 1993 ident: 10.1016/j.ejor.2014.11.031_bib0066 article-title: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition publication-title: 27th asilomar conference on signals, systems and computers doi: 10.1109/ACSSC.1993.342465 – year: 2008 ident: 10.1016/j.ejor.2014.11.031_bib0009 article-title: Iteratively reweighted algorithms for compressive sensing publication-title: ICASSP – year: 2002 ident: 10.1016/j.ejor.2014.11.031_bib0069 article-title: DC programming: Theory, algorithms and applications. The state of the art – start-page: 175 year: 1996 ident: 10.1016/j.ejor.2014.11.031_bib0060 article-title: Machine learning via polyhedral concave minimization – year: 2012 ident: 10.1016/j.ejor.2014.11.031_bib0043 – year: 2010 ident: 10.1016/j.ejor.2014.11.031_bib0080 article-title: Learning sparse svm for feature selection on very high dimensional datasets. publication-title: ICML 2010 – volume: 52 start-page: 509 issue: 3 year: 2012 ident: 10.1016/j.ejor.2014.11.031_bib0044 article-title: Exact penalty and error bounds in DC programming publication-title: Journal of Global Optimization doi: 10.1007/s10898-011-9765-3 – volume: 8 start-page: 476 year: 1998 ident: 10.1016/j.ejor.2014.11.031_bib0068 article-title: DC optimization algorithms for solving the trust region subproblem. publication-title: SIAM Journal of Optimization doi: 10.1137/S1052623494274313 – volume: 8342 start-page: 1 year: 2014 ident: 10.1016/j.ejor.2014.11.031_bib0071 article-title: Recent advances in DC programming and DCA publication-title: Transactions on Computational Collective Intelligence – volume: 96 start-page: 1348 issue: 456 year: 2001 ident: 10.1016/j.ejor.2014.11.031_bib0014 article-title: Variable selection via nonconcave penalized likelihood and its oracle properties publication-title: Journal of the American Statistical Association doi: 10.1198/016214501753382273 – volume: 183 start-page: 1067 year: 2007 ident: 10.1016/j.ejor.2014.11.031_bib0038 article-title: Optimization based DC programming and DCA for hierarchical clustering publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2005.07.028 – year: 2004 ident: 10.1016/j.ejor.2014.11.031_bib0025 article-title: Leveraging the margin more carefully – volume: 156 start-page: 325 year: 2008 ident: 10.1016/j.ejor.2014.11.031_bib0039 article-title: A continuous approach for the concave cost supply problem via DC programming and DCA publication-title: Discrete Applied Mathematics doi: 10.1016/j.dam.2007.03.024 – volume: 24 start-page: 227 year: 1995 ident: 10.1016/j.ejor.2014.11.031_bib0062 article-title: Sparse approximate solutions to linear systems publication-title: SIAM Journal on Scientific Computing doi: 10.1137/S0097539792240406 – volume: 45 start-page: 600 year: 1997 ident: 10.1016/j.ejor.2014.11.031_bib0019 article-title: Sparse signal reconstructions from limited data using FOCUSS: A re-weighted minimum norm algorithm publication-title: IEEE Transactions on Signal Processing doi: 10.1109/78.558475 – start-page: 1063 year: 2010 ident: 10.1016/j.ejor.2014.11.031_bib0082 article-title: A DC programming approach for sparse eigenvalue problem. – volume: 47 start-page: 87 year: 1999 ident: 10.1016/j.ejor.2014.11.031_bib0073 article-title: An affine scaling methodology for best basis selection publication-title: IEEE Transactions on Signal Processing doi: 10.1109/78.738251 – volume: 20 start-page: 273 issue: 3 year: 1995 ident: 10.1016/j.ejor.2014.11.031_bib0012 article-title: Support vector networks publication-title: Machine Learning doi: 10.1007/BF00994018 – volume: 56 start-page: 1393 issue: 4 year: 2013 ident: 10.1016/j.ejor.2014.11.031_bib0048 article-title: Binary classification via spherical separator by DC programming and DCA publication-title: Journal of Global Optimization doi: 10.1007/s10898-012-9859-6 – year: 1997 ident: 10.1016/j.ejor.2014.11.031_bib0028 – ident: 10.1016/j.ejor.2014.11.031_bib0031 – start-page: 528 year: 2013 ident: 10.1016/j.ejor.2014.11.031_bib0047 article-title: DCA based algorithms for feature selection in semi-supervised support vector machines – start-page: 348 year: 2008 ident: 10.1016/j.ejor.2014.11.031_bib0081 article-title: DC programming approach for solving a class of nonconvex programs dealing with zero-norm – volume: 41 start-page: 3397 issue: 12 year: 1993 ident: 10.1016/j.ejor.2014.11.031_bib0059 article-title: Matching pursuit in a time-frequency dictionary publication-title: IEEE Transactions on Signal Processing doi: 10.1109/78.258082 – start-page: 831 year: 2007 ident: 10.1016/j.ejor.2014.11.031_bib0078 article-title: Sparse eigen methods by D.C. programming – volume: 133 start-page: 23 year: 2005 ident: 10.1016/j.ejor.2014.11.031_bib0034 article-title: The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems publication-title: Annals of Operations Research doi: 10.1007/s10479-004-5022-1 – volume: 51 start-page: 1097 issue: 3 year: 2012 ident: 10.1016/j.ejor.2014.11.031_bib0045 article-title: A DC programming approach for solving the symmetric eigenvalue complementarity problem publication-title: Computational Optimization and Applications doi: 10.1007/s10589-010-9388-5 – volume: 183 start-page: 1001 year: 2007 ident: 10.1016/j.ejor.2014.11.031_bib0037 article-title: A continuous approach for solving the concave cost supply problem by combining DCA and B&B techniques publication-title: European Journal of Operational Research – volume: 59 start-page: 2277 issue: 8 year: 2014 ident: 10.1016/j.ejor.2014.11.031_bib0072 article-title: A DC programming algorithm for switched linear regression publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2014.2301575 – year: 1994 ident: 10.1016/j.ejor.2014.11.031_bib0027 – volume: 32 start-page: 2832 issue: 5 year: 2010 ident: 10.1016/j.ejor.2014.11.031_bib0011 article-title: Lower bound theory of nonzero entries in solutions of l2-lp minimization publication-title: SIAM Journal on Scientific Computing doi: 10.1137/090761471 – year: 2014 ident: 10.1016/j.ejor.2014.11.031_bib0015 article-title: Dictionary learning for fast classification based on soft-thresholding publication-title: International Journal of Computer Vision doi: 10.1007/s11263-014-0784-7 – year: 2006 ident: 10.1016/j.ejor.2014.11.031_bib0079 article-title: A new compressive imaging camera architecture using optical-domain compression – volume: 3 start-page: 62 issue: 6 year: 2009 ident: 10.1016/j.ejor.2014.11.031_bib0041 article-title: Gene selection for cancer classification using DCA publication-title: Journal of Fonctiers of Computer Science and Technology – volume: 37 start-page: 2109 year: 2009 ident: 10.1016/j.ejor.2014.11.031_bib0087 article-title: Some sharp performance bounds for least squares regression with regularization publication-title: Annals of Statistics doi: 10.1214/08-AOS659 – volume: 25 start-page: 2776 issue: 10 year: 2013 ident: 10.1016/j.ejor.2014.11.031_bib0046 article-title: Block clustering based on difference of convex functions (DC) programming and DC algorithms publication-title: Neural Computation doi: 10.1162/NECO_a_00490 – volume: 1 start-page: 23 year: 1992 ident: 10.1016/j.ejor.2014.11.031_bib0003 article-title: Robust linear programming discrimination of two linearly inseparable sets publication-title: Optimization Methods and Software doi: 10.1080/10556789208805504 – year: 2006 ident: 10.1016/j.ejor.2014.11.031_bib0021 – start-page: 145 year: 2007 ident: 10.1016/j.ejor.2014.11.031_bib0010 article-title: Direct convex relaxations of sparse SVM
SSID	ssj0001515
Score	2.5726247
Snippet	•A unifying DC approximation, including all standard approximations, of the zero-norm is proposed.•The consistency between global/local minima of approximate... Sparse optimization refers to an optimization problem involving the zero-norm in objective or constraints. In this paper, nonconvex approximation approaches...
SourceID	hal proquest crossref elsevier
SourceType	Open Access Repository Aggregation Database Enrichment Source Index Database Publisher
StartPage	26
SubjectTerms	Algorithms Approximation Computer Science DC approximation function DC programming and DCA Direct current Feature selection in SVM Global optimization Mathematical analysis Mathematical functions Mathematical models Mathematical problems Mathematical programming Optimization Optimization algorithms Programming Sparse optimization Studies
Title	DC approximation approaches for sparse optimization
URI	https://dx.doi.org/10.1016/j.ejor.2014.11.031 https://www.proquest.com/docview/1664475767 https://www.proquest.com/docview/1685828368 https://hal.univ-lorraine.fr/hal-01616997
Volume	244
WOSCitedRecordID	wos000352667900004&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/eLvHCXMwtV3db9owELconabtYR9dp7F1UzZNe0FBjR079iMCqm5CrFLTijcrH44AtYFSQPwZ-5N3jp0A1Yq2h71EiWMn4Lvcnc93v0PoK6YZWNmCuVh5qetzmrqxUJkrooSB8CM-Lcq9XfeDwYAPh-KiVvtV5sKsboI85-u1mP1XUkMbEFunzv4DuauHQgOcA9HhCGSH418RvtsxQOHrsclKrGDDVQG90AQRMtcOfJAVtzYJ81H3vDVVoWFeOg0tOlDlRQ5Hpu51u9mveKQ7zgtvTdi8GEW3m5ifXdzs68JLOwy3HQ8erYJUrTeszIi5VHfL6ThqXrbaW8KLBIGLscnLbCkjXHmAXcZN_YBS-mID_7jDZlaWsj-KeONtmLTUZKrxXD2_pVFYrSrZwdMe_JRnV_2-DHvDcPeu0d8MFpuY-ML7NrtzdR0yvV9vi7IcoEMcUMHr6LD9vTf8UWl3bQAWO1P2_9lELBMz-PA3PWbsHIx01O0D5V9YNOEr9MIuRZy2YaHXqKbyI_S0zIQ4Qi_Lih-OVQBH6PkWfOUbRLodZ4fVnA2rOcBqjmE1Z5vVjtHVWS_snLu2CIebgGm8cDOmuPCjUxInfpBq-zXJFE590AyEelnECKZwnUaYpzFWiV6fKkJFwkWCwdgnb1E9n-bqHXJinwYC630-rPxYpXF6SiLKMYWRqZ_xBvLK2ZKJRajXhVJuZBmKOJF6hqWeYVi6SpjhBmpWY2YGn2Vvb1oSQVoL01iOErhr77gvQLHqBRqS_bzdl7pNL5lAqgUr6HRSElTaT_ReeoxpSM2ABQ30uboNIlzvy0W5mi51H643rwnj7_c_4gN6tvkQT1B9MV-qj-hJslqM7-efLKf-BhPzuA4
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=DC+approximation+approaches+for+sparse+optimization&rft.jtitle=European+journal+of+operational+research&rft.au=Thi%2C+HA+Le&rft.au=Dinh%2C+T+Pham&rft.au=Le%2C+HM&rft.au=Vo%2C+XT&rft.date=2015-07-01&rft.pub=Elsevier+Sequoia+S.A&rft.issn=0377-2217&rft.eissn=1872-6860&rft.volume=244&rft.issue=1&rft.spage=26&rft_id=info:doi/10.1016%2Fj.ejor.2014.11.031&rft.externalDBID=NO_FULL_TEXT&rft.externalDocID=3628923491
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