Robust monotone submodular function maximization
We consider a robust formulation, introduced by Krause et al. (J Artif Intell Res 42:427–486, 2011 ), of the classical cardinality constrained monotone submodular function maximization problem, and give the first constant factor approximation results. The robustness considered is w.r.t. adversarial...
Gespeichert in:
| Veröffentlicht in: | Mathematical programming Jg. 172; H. 1-2; S. 505 - 537 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2018
Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 0025-5610, 1436-4646 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | We consider a robust formulation, introduced by Krause et al. (J Artif Intell Res 42:427–486,
2011
), of the classical cardinality constrained monotone submodular function maximization problem, and give the first constant factor approximation results. The robustness considered is w.r.t. adversarial removal of up to
τ
elements from the chosen set. For the fundamental case of
τ
=
1
, we give a deterministic
(
1
-
1
/
e
)
-
1
/
Θ
(
m
)
approximation algorithm, where
m
is an input parameter and number of queries scale as
O
(
n
m
+
1
)
. In the process, we develop a deterministic
(
1
-
1
/
e
)
-
1
/
Θ
(
m
)
approximate greedy algorithm for bi-objective maximization of (two) monotone submodular functions. Generalizing the ideas and using a result from Chekuri et al. (in: FOCS 10, IEEE, pp 575–584,
2010
), we show a randomized
(
1
-
1
/
e
)
-
ϵ
approximation for constant
τ
and
ϵ
≤
1
Ω
~
(
τ
)
, making
O
(
n
1
/
ϵ
3
)
queries. Further, for
τ
≪
k
, we give a fast and practical 0.387 algorithm. Finally, we also give a black box result result for the much more general setting of robust maximization subject to an Independence System. |
|---|---|
| AbstractList | We consider a robust formulation, introduced by Krause et al. (J Artif Intell Res 42:427–486, 2011), of the classical cardinality constrained monotone submodular function maximization problem, and give the first constant factor approximation results. The robustness considered is w.r.t. adversarial removal of up to \[\tau \] elements from the chosen set. For the fundamental case of \[\tau =1\], we give a deterministic \[(1-1/e)-1/\varTheta (m)\] approximation algorithm, where m is an input parameter and number of queries scale as \[O(n^{m+1})\]. In the process, we develop a deterministic \[(1-1/e)-1/\varTheta (m)\] approximate greedy algorithm for bi-objective maximization of (two) monotone submodular functions. Generalizing the ideas and using a result from Chekuri et al. (in: FOCS 10, IEEE, pp 575–584, 2010), we show a randomized \[(1-1/e)-\epsilon \] approximation for constant \[\tau \] and \[\epsilon \le \frac{1}{\tilde{\varOmega }(\tau )}\], making \[O(n^{1/\epsilon ^3})\] queries. Further, for \[\tau \ll \sqrt{k}\], we give a fast and practical 0.387 algorithm. Finally, we also give a black box result result for the much more general setting of robust maximization subject to an Independence System. We consider a robust formulation, introduced by Krause et al. (J Artif Intell Res 42:427–486, 2011 ), of the classical cardinality constrained monotone submodular function maximization problem, and give the first constant factor approximation results. The robustness considered is w.r.t. adversarial removal of up to τ elements from the chosen set. For the fundamental case of τ = 1 , we give a deterministic ( 1 - 1 / e ) - 1 / Θ ( m ) approximation algorithm, where m is an input parameter and number of queries scale as O ( n m + 1 ) . In the process, we develop a deterministic ( 1 - 1 / e ) - 1 / Θ ( m ) approximate greedy algorithm for bi-objective maximization of (two) monotone submodular functions. Generalizing the ideas and using a result from Chekuri et al. (in: FOCS 10, IEEE, pp 575–584, 2010 ), we show a randomized ( 1 - 1 / e ) - ϵ approximation for constant τ and ϵ ≤ 1 Ω ~ ( τ ) , making O ( n 1 / ϵ 3 ) queries. Further, for τ ≪ k , we give a fast and practical 0.387 algorithm. Finally, we also give a black box result result for the much more general setting of robust maximization subject to an Independence System. |
| Author | Schulz, Andreas S. Orlin, James B. Udwani, Rajan |
| Author_xml | – sequence: 1 givenname: James B. surname: Orlin fullname: Orlin, James B. organization: M.I.T – sequence: 2 givenname: Andreas S. surname: Schulz fullname: Schulz, Andreas S. organization: M.I.T – sequence: 3 givenname: Rajan orcidid: 0000-0002-2112-4876 surname: Udwani fullname: Udwani, Rajan email: rudwani@alum.mit.edu organization: M.I.T |
| BookMark | eNp9kMFKxDAQhoOs4O7qA3greI7OJG3aHmVRV1gQRM9hmqTSZdusSQvq09u1giDoaRj4v3-Gb8Fmne8cY-cIlwiQX0UEhJwDFhylAC6O2BxTqXiqUjVjcwCR8UwhnLBFjFsAQFkUcwaPvhpin7S-8_1YmcShar0ddhSSeuhM3_guaemtaZsPOiyn7LimXXRn33PJnm9vnlZrvnm4u19db7iRqHqeoa2FEza3UjlFxmBNtkIEIxU5l5JxOaFQGVa2MkVOJVFqs8rVlS1KKuWSXUy9--BfBxd7vfVD6MaTWiDIsshEno6pfEqZ4GMMrtam6b_-7AM1O42gD3r0pEePevRBjxYjib_IfWhaCu__MmJi4pjtXlz4-elv6BO3p3qD |
| CitedBy_id | crossref_primary_10_1016_j_ejor_2023_04_024 crossref_primary_10_1109_TMC_2020_3043000 crossref_primary_10_1049_rpg2_12779 crossref_primary_10_1287_opre_2021_2145 crossref_primary_10_1109_TRO_2022_3161765 crossref_primary_10_1145_3419755 crossref_primary_10_1109_TRO_2021_3082212 crossref_primary_10_1137_23M1569265 crossref_primary_10_1016_j_tcs_2022_09_029 crossref_primary_10_1137_22M1526952 crossref_primary_10_1109_TRO_2022_3233341 crossref_primary_10_1287_moor_2022_0320 crossref_primary_10_1287_opre_2021_2180 crossref_primary_10_3390_app15158217 crossref_primary_10_26599_TST_2023_9010026 crossref_primary_10_1287_ijoo_2019_0041 crossref_primary_10_1016_j_tcs_2024_114755 crossref_primary_10_1145_3698397 crossref_primary_10_1109_JIOT_2021_3078620 crossref_primary_10_1109_TMC_2021_3136868 |
| Cites_doi | 10.1137/080734510 10.1007/BF01588971 10.1515/9781400831050 10.1109/ICASSP.2013.6639057 10.1137/1.9781611973082.83 10.1145/285055.285059 10.1137/090779346 10.1137/1.9781611972795.92 10.1137/1.9781611973402.110 10.1109/FOCS.2011.46 10.1145/1993636.1993740 10.1145/1102351.1102385 10.1145/502090.502096 10.1016/S0167-6377(03)00062-2 10.1007/s10107-003-0396-4 10.1109/FOCS.2010.60 10.1145/1143844.1143889 10.1137/1.9781611974331.ch29 10.1287/moor.3.3.177 10.1145/1374376.1374389 10.1287/opre.1030.0065 10.1145/1127777.1127782 10.1007/978-3-642-22006-7_29 10.1137/110832318 10.1109/FOCS.2012.73 10.1006/jctb.2000.1989 10.1137/080733991 10.1145/2213977.2214076 10.1145/1281192.1281239 |
| ContentType | Journal Article |
| Copyright | Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society 2018 Mathematical Programming is a copyright of Springer, (2018). All Rights Reserved. |
| Copyright_xml | – notice: Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society 2018 – notice: Mathematical Programming is a copyright of Springer, (2018). All Rights Reserved. |
| DBID | AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 88I 8AL 8AO 8FD 8FE 8FG 8FK 8FL ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ K7- L.- L6V L7M L~C L~D M0C M0N M2P M7S P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PRINS PTHSS Q9U |
| DOI | 10.1007/s10107-018-1320-2 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Science Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Business Premium Collection ProQuest Technology Collection ProQuest One ProQuest Central Korea Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database (ProQuest) ABI/INFORM Professional Advanced ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Science Database Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) Proquest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection ProQuest Central Basic |
| DatabaseTitle | CrossRef ProQuest Business Collection (Alumni Edition) Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ABI/INFORM Complete ProQuest One Applied & Life Sciences ProQuest Central (New) Engineering Collection Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global Engineering Database ProQuest Science Journals (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest Business Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ABI/INFORM Global (Corporate) ProQuest One Business Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Pharma Collection ProQuest Central ABI/INFORM Professional Advanced ProQuest Engineering Collection ProQuest Central Korea Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Science Journals ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection ProQuest One Business (Alumni) ProQuest Central (Alumni) Business Premium Collection (Alumni) |
| DatabaseTitleList | ProQuest Business Collection (Alumni Edition) |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central Database Suite (ProQuest) url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Mathematics |
| EISSN | 1436-4646 |
| EndPage | 537 |
| ExternalDocumentID | 10_1007_s10107_018_1320_2 |
| GrantInformation_xml | – fundername: Office of Naval Research Global grantid: 6929031 funderid: http://dx.doi.org/10.13039/100007297 |
| GroupedDBID | --K --Z -52 -5D -5G -BR -EM -Y2 -~C -~X .4S .86 .DC .VR 06D 0R~ 0VY 199 1B1 1N0 1OL 1SB 203 28- 29M 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 6TJ 78A 7WY 88I 8AO 8FE 8FG 8FL 8TC 8UJ 8VB 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTV ABHLI ABHQN ABJCF ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACGOD ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACNCT ACOKC ACOMO ACPIV ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMOZ AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFFNX AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHQJS AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ AKVCP ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. B0M BA0 BAPOH BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EAD EAP EBA EBLON EBR EBS EBU ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ H~9 I-F I09 IAO IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K1G K60 K6V K6~ K7- KDC KOV KOW L6V LAS LLZTM M0C M0N M2P M4Y M7S MA- N2Q N9A NB0 NDZJH NPVJJ NQ- NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9R PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PT5 PTHSS Q2X QOK QOS QWB R4E R89 R9I RHV RIG RNI RNS ROL RPX RPZ RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCLPG SDD SDH SDM SHX SISQX SJYHP SMT SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TH9 TN5 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WH7 WK8 XPP YLTOR Z45 Z5O Z7R Z7S Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8R Z8T Z8W Z92 ZL0 ZMTXR ZWQNP ~02 ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADXHL AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP AMVHM ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 7SC 7XB 8AL 8FD 8FK JQ2 L.- L7M L~C L~D PKEHL PQEST PQUKI PRINS PUEGO Q9U |
| ID | FETCH-LOGICAL-c316t-51df2e2d7d36e6acc1fadb110c36aee4ace7a12651bdbc87a9aa4d5befbd89a93 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 38 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000447751100023&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0025-5610 |
| IngestDate | Thu Sep 25 00:52:22 EDT 2025 Sat Nov 29 03:34:00 EST 2025 Tue Nov 18 22:43:18 EST 2025 Fri Feb 21 02:32:37 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1-2 |
| Keywords | 90 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c316t-51df2e2d7d36e6acc1fadb110c36aee4ace7a12651bdbc87a9aa4d5befbd89a93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-2112-4876 |
| PQID | 2103985274 |
| PQPubID | 25307 |
| PageCount | 33 |
| ParticipantIDs | proquest_journals_2103985274 crossref_citationtrail_10_1007_s10107_018_1320_2 crossref_primary_10_1007_s10107_018_1320_2 springer_journals_10_1007_s10107_018_1320_2 |
| PublicationCentury | 2000 |
| PublicationDate | 20181100 2018-11-00 20181101 |
| PublicationDateYYYYMMDD | 2018-11-01 |
| PublicationDate_xml | – month: 11 year: 2018 text: 20181100 |
| PublicationDecade | 2010 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Heidelberg |
| PublicationSubtitle | A Publication of the Mathematical Optimization Society |
| PublicationTitle | Mathematical programming |
| PublicationTitleAbbrev | Math. Program |
| PublicationYear | 2018 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
| References | Feldman, M., Naor, J.S., Schwartz, R.: Nonmonotone submodular maximization via a structural continuous greedy algorithm. In: Automata, Languages and Programming, pp. 342–353. Springer (2011) KrauseAMcMahanHBGuestrinCGuptaARobust submodular observation selectionJ. Mach. Learn. Res.20089276128011225.90107 CalinescuGChekuriCPálMVondrákJMaximizing a monotone submodular function subject to a matroid constraintSIAM J. Comput.201140617401766286319310.1137/080733991 GolovinDKrauseAAdaptive submodularity: Theory and applications in active learning and stochastic optimizationJ. Artif. Intell. Res.20114242748628748071230.90141 NemhauserGLWolseyLAFisherMLAn analysis of approximations for maximizing submodular set functions–iMath. Program.197814126529450386610.1007/BF01588971 FeigeUA threshold of ln n for approximating set coverJ. ACM (JACM)199845463465210.1145/285055.285059 Guestrin, C., Krause, A., Singh, A.P.: Near-optimal sensor placements in gaussian processes. In: Proceedings of the 22nd International Conference on Machine learning, pp. 265–272. ACM (2005) SchrijverAA combinatorial algorithm minimizing submodular functions in strongly polynomial timeJ. Comb. Theory, Ser. B2000802346355179469810.1006/jctb.2000.1989 Vondrák, J.: Optimal approximation for the submodular welfare problem in the value oracle model. In: STOC ’08, pp. 67–74. ACM (2008) Buchbinder, N., Feldman, M.: Deterministic algorithms for submodular maximization problems. CoRR. arXiv:1508.02157 (2015) Chekuri, C., Vondrák, J., Zenklusen, R.: Dependent randomized rounding via exchange properties of combinatorial structures. In: FOCS 10, pp. 575–584. IEEE (2010) Buchbinder, N., Feldman, M., Naor, J.S., Schwartz, R.: A tight linear time (1/2)-approximation for unconstrained submodular maximization. In: FOCS ’12, pp. 649–658 (2012) IwataSFleischerLFujishigeSA combinatorial strongly polynomial algorithm for minimizing submodular functionsJ. ACM (JACM)2001484761777214492910.1145/502090.502096 FeigeUMirrokniVSVondrakJMaximizing non-monotone submodular functionsSIAM J. Comput.201140411331153282531210.1137/090779346 BertsimasDSimMThe price of robustnessOper. Res.20045213553206623910.1287/opre.1030.0065 VondrákJSymmetry and approximability of submodular maximization problemsSIAM J. Comput.2013421265304303312910.1137/110832318 SviridenkoMA note on maximizing a submodular set function subject to a knapsack constraintOper. Res. Lett.20043214143201710710.1016/S0167-6377(03)00062-2 Badanidiyuru, A., Vondrák, J.: Fast algorithms for maximizing submodular functions. In SODA ’14, pages 1497–1514. SIAM, (2014) Vondrák, J., Chekuri, C., Zenklusen, R.: Submodular function maximization via the multilinear relaxation and contention resolution schemes. In: STOC ’11, pp. 783–792. ACM (2011) Ben-TalAEl GhaouiLNemirovskiARobust optimization2009PrincetonPrinceton University Press10.1515/9781400831050 Dobzinski, S., Vondrák, J.: From query complexity to computational complexity. In: STOC ’12, pp. 1107–1116. ACM (2012) Globerson, A., Roweis, S.: Nightmare at test time: robust learning by feature deletion. In: Proceedings of the 23rd International Conference on Machine learning, pp. 353–360. ACM (2006) Feldman, M., Naor, J.S., Schwartz, R.: A unified continuous greedy algorithm for submodular maximization. In: FOCS ’11, pp. 570–579. IEEE (2011) Thoma, M., Cheng, H., Gretton, A., Han, J., Kriegel, H.P., Smola, A.J., Song, L., Philip, S.Y., Yan, X., Borgwardt, K.M.: Near-optimal supervised feature selection among frequent subgraphs. In: SDM, pp. 1076–1087. SIAM (2009) BertsimasDSimMRobust discrete optimization and network flowsMath. Program.2003981–34971201936710.1007/s10107-003-0396-4 Gharan, S.O., Vondrák, J.: Submodular maximization by simulated annealing. In: SODA ’11, pp. 1098–1116. SIAM (2011) Liu, Y., Wei, K., Kirchhoff, K., Song, Y., Bilmes, J.: Submodular feature selection for high-dimensional acoustic score spaces. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7184–7188. IEEE (2013) NemhauserGLWolseyLABest algorithms for approximating the maximum of a submodular set functionMath. Oper. Res.19783317718850665610.1287/moor.3.3.177 Bogunovic, I., Mitrovic, S., Scarlett, J., Cevher, V.: Robust submodular maximization: a non-uniform partitioning approach. In: ICML (2017) Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge discovery and Data Mining, pp. 420–429. ACM (2007) BertsimasDBrownDCaramanisCTheory and applications of robust optimizationSIAM Rev.2011533464501283408410.1137/080734510 Krause, A., Guestrin, C., Gupta, A., Kleinberg, J.: Near-optimal sensor placements: maximizing information while minimizing communication cost. In: Proceedings of the 5th International Conference On Information processing in Sensor Networks, pp. 2–10. ACM (2006) 1320_CR23 1320_CR21 M Sviridenko (1320_CR28) 2004; 32 G Calinescu (1320_CR9) 2011; 40 1320_CR8 GL Nemhauser (1320_CR26) 1978; 14 1320_CR6 D Bertsimas (1320_CR4) 2003; 98 1320_CR7 A Ben-Tal (1320_CR2) 2009 D Bertsimas (1320_CR3) 2011; 53 1320_CR1 1320_CR29 1320_CR24 J Vondrák (1320_CR31) 2013; 42 D Bertsimas (1320_CR5) 2004; 52 1320_CR11 1320_CR10 1320_CR32 D Golovin (1320_CR18) 2011; 42 1320_CR30 A Schrijver (1320_CR27) 2000; 80 S Iwata (1320_CR20) 2001; 48 GL Nemhauser (1320_CR25) 1978; 3 A Krause (1320_CR22) 2008; 9 U Feige (1320_CR12) 1998; 45 1320_CR19 1320_CR17 1320_CR16 1320_CR15 1320_CR14 U Feige (1320_CR13) 2011; 40 |
| References_xml | – reference: KrauseAMcMahanHBGuestrinCGuptaARobust submodular observation selectionJ. Mach. Learn. Res.20089276128011225.90107 – reference: GolovinDKrauseAAdaptive submodularity: Theory and applications in active learning and stochastic optimizationJ. Artif. Intell. Res.20114242748628748071230.90141 – reference: Feldman, M., Naor, J.S., Schwartz, R.: Nonmonotone submodular maximization via a structural continuous greedy algorithm. In: Automata, Languages and Programming, pp. 342–353. Springer (2011) – reference: Globerson, A., Roweis, S.: Nightmare at test time: robust learning by feature deletion. In: Proceedings of the 23rd International Conference on Machine learning, pp. 353–360. ACM (2006) – reference: Feldman, M., Naor, J.S., Schwartz, R.: A unified continuous greedy algorithm for submodular maximization. In: FOCS ’11, pp. 570–579. IEEE (2011) – reference: SviridenkoMA note on maximizing a submodular set function subject to a knapsack constraintOper. Res. Lett.20043214143201710710.1016/S0167-6377(03)00062-2 – reference: Liu, Y., Wei, K., Kirchhoff, K., Song, Y., Bilmes, J.: Submodular feature selection for high-dimensional acoustic score spaces. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7184–7188. IEEE (2013) – reference: Buchbinder, N., Feldman, M., Naor, J.S., Schwartz, R.: A tight linear time (1/2)-approximation for unconstrained submodular maximization. In: FOCS ’12, pp. 649–658 (2012) – reference: NemhauserGLWolseyLABest algorithms for approximating the maximum of a submodular set functionMath. Oper. Res.19783317718850665610.1287/moor.3.3.177 – reference: SchrijverAA combinatorial algorithm minimizing submodular functions in strongly polynomial timeJ. Comb. Theory, Ser. B2000802346355179469810.1006/jctb.2000.1989 – reference: Vondrák, J.: Optimal approximation for the submodular welfare problem in the value oracle model. In: STOC ’08, pp. 67–74. ACM (2008) – reference: Buchbinder, N., Feldman, M.: Deterministic algorithms for submodular maximization problems. CoRR. arXiv:1508.02157 (2015) – reference: FeigeUA threshold of ln n for approximating set coverJ. ACM (JACM)199845463465210.1145/285055.285059 – reference: Dobzinski, S., Vondrák, J.: From query complexity to computational complexity. In: STOC ’12, pp. 1107–1116. ACM (2012) – reference: FeigeUMirrokniVSVondrakJMaximizing non-monotone submodular functionsSIAM J. Comput.201140411331153282531210.1137/090779346 – reference: IwataSFleischerLFujishigeSA combinatorial strongly polynomial algorithm for minimizing submodular functionsJ. ACM (JACM)2001484761777214492910.1145/502090.502096 – reference: Thoma, M., Cheng, H., Gretton, A., Han, J., Kriegel, H.P., Smola, A.J., Song, L., Philip, S.Y., Yan, X., Borgwardt, K.M.: Near-optimal supervised feature selection among frequent subgraphs. In: SDM, pp. 1076–1087. SIAM (2009) – reference: Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge discovery and Data Mining, pp. 420–429. ACM (2007) – reference: Krause, A., Guestrin, C., Gupta, A., Kleinberg, J.: Near-optimal sensor placements: maximizing information while minimizing communication cost. In: Proceedings of the 5th International Conference On Information processing in Sensor Networks, pp. 2–10. ACM (2006) – reference: Ben-TalAEl GhaouiLNemirovskiARobust optimization2009PrincetonPrinceton University Press10.1515/9781400831050 – reference: VondrákJSymmetry and approximability of submodular maximization problemsSIAM J. Comput.2013421265304303312910.1137/110832318 – reference: Guestrin, C., Krause, A., Singh, A.P.: Near-optimal sensor placements in gaussian processes. In: Proceedings of the 22nd International Conference on Machine learning, pp. 265–272. ACM (2005) – reference: BertsimasDBrownDCaramanisCTheory and applications of robust optimizationSIAM Rev.2011533464501283408410.1137/080734510 – reference: NemhauserGLWolseyLAFisherMLAn analysis of approximations for maximizing submodular set functions–iMath. Program.197814126529450386610.1007/BF01588971 – reference: Badanidiyuru, A., Vondrák, J.: Fast algorithms for maximizing submodular functions. In SODA ’14, pages 1497–1514. SIAM, (2014) – reference: BertsimasDSimMRobust discrete optimization and network flowsMath. Program.2003981–34971201936710.1007/s10107-003-0396-4 – reference: Chekuri, C., Vondrák, J., Zenklusen, R.: Dependent randomized rounding via exchange properties of combinatorial structures. In: FOCS 10, pp. 575–584. IEEE (2010) – reference: CalinescuGChekuriCPálMVondrákJMaximizing a monotone submodular function subject to a matroid constraintSIAM J. Comput.201140617401766286319310.1137/080733991 – reference: Gharan, S.O., Vondrák, J.: Submodular maximization by simulated annealing. In: SODA ’11, pp. 1098–1116. SIAM (2011) – reference: Vondrák, J., Chekuri, C., Zenklusen, R.: Submodular function maximization via the multilinear relaxation and contention resolution schemes. In: STOC ’11, pp. 783–792. ACM (2011) – reference: Bogunovic, I., Mitrovic, S., Scarlett, J., Cevher, V.: Robust submodular maximization: a non-uniform partitioning approach. In: ICML (2017) – reference: BertsimasDSimMThe price of robustnessOper. Res.20045213553206623910.1287/opre.1030.0065 – volume: 53 start-page: 464 issue: 3 year: 2011 ident: 1320_CR3 publication-title: SIAM Rev. doi: 10.1137/080734510 – volume: 14 start-page: 265 issue: 1 year: 1978 ident: 1320_CR26 publication-title: Math. Program. doi: 10.1007/BF01588971 – volume-title: Robust optimization year: 2009 ident: 1320_CR2 doi: 10.1515/9781400831050 – ident: 1320_CR24 doi: 10.1109/ICASSP.2013.6639057 – ident: 1320_CR16 doi: 10.1137/1.9781611973082.83 – volume: 45 start-page: 634 issue: 4 year: 1998 ident: 1320_CR12 publication-title: J. ACM (JACM) doi: 10.1145/285055.285059 – volume: 40 start-page: 1133 issue: 4 year: 2011 ident: 1320_CR13 publication-title: SIAM J. Comput. doi: 10.1137/090779346 – ident: 1320_CR6 – ident: 1320_CR29 doi: 10.1137/1.9781611972795.92 – ident: 1320_CR1 doi: 10.1137/1.9781611973402.110 – ident: 1320_CR14 doi: 10.1109/FOCS.2011.46 – ident: 1320_CR32 doi: 10.1145/1993636.1993740 – ident: 1320_CR19 doi: 10.1145/1102351.1102385 – volume: 48 start-page: 761 issue: 4 year: 2001 ident: 1320_CR20 publication-title: J. ACM (JACM) doi: 10.1145/502090.502096 – volume: 32 start-page: 41 issue: 1 year: 2004 ident: 1320_CR28 publication-title: Oper. Res. Lett. doi: 10.1016/S0167-6377(03)00062-2 – volume: 98 start-page: 49 issue: 1–3 year: 2003 ident: 1320_CR4 publication-title: Math. Program. doi: 10.1007/s10107-003-0396-4 – ident: 1320_CR10 doi: 10.1109/FOCS.2010.60 – ident: 1320_CR17 doi: 10.1145/1143844.1143889 – ident: 1320_CR7 doi: 10.1137/1.9781611974331.ch29 – volume: 3 start-page: 177 issue: 3 year: 1978 ident: 1320_CR25 publication-title: Math. Oper. Res. doi: 10.1287/moor.3.3.177 – ident: 1320_CR30 doi: 10.1145/1374376.1374389 – volume: 52 start-page: 35 issue: 1 year: 2004 ident: 1320_CR5 publication-title: Oper. Res. doi: 10.1287/opre.1030.0065 – ident: 1320_CR21 doi: 10.1145/1127777.1127782 – ident: 1320_CR15 doi: 10.1007/978-3-642-22006-7_29 – volume: 42 start-page: 265 issue: 1 year: 2013 ident: 1320_CR31 publication-title: SIAM J. Comput. doi: 10.1137/110832318 – ident: 1320_CR8 doi: 10.1109/FOCS.2012.73 – volume: 80 start-page: 346 issue: 2 year: 2000 ident: 1320_CR27 publication-title: J. Comb. Theory, Ser. B doi: 10.1006/jctb.2000.1989 – volume: 40 start-page: 1740 issue: 6 year: 2011 ident: 1320_CR9 publication-title: SIAM J. Comput. doi: 10.1137/080733991 – ident: 1320_CR11 doi: 10.1145/2213977.2214076 – volume: 42 start-page: 427 year: 2011 ident: 1320_CR18 publication-title: J. Artif. Intell. Res. – volume: 9 start-page: 2761 year: 2008 ident: 1320_CR22 publication-title: J. Mach. Learn. Res. – ident: 1320_CR23 doi: 10.1145/1281192.1281239 |
| SSID | ssj0001388 |
| Score | 2.4869187 |
| Snippet | We consider a robust formulation, introduced by Krause et al. (J Artif Intell Res 42:427–486,
2011
), of the classical cardinality constrained monotone... We consider a robust formulation, introduced by Krause et al. (J Artif Intell Res 42:427–486, 2011), of the classical cardinality constrained monotone... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 505 |
| SubjectTerms | Algorithms Approximation Calculus of Variations and Optimal Control; Optimization Combinatorics Full Length Paper Greedy algorithms Mathematical analysis Mathematical and Computational Physics Mathematical Methods in Physics Mathematics Mathematics and Statistics Mathematics of Computing Maximization Numerical Analysis Queries Robustness Theoretical |
| SummonAdditionalLinks | – databaseName: ABI/INFORM Collection dbid: 7WY link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NS8MwFH_o9KAHv8XplB48KcUlWZr0JCIODzpEFOep5KswcOtcO_HPN-nSVQV38VjapE3e6_vM-z2AU00QxpZZQokZtg4KY6GMXIEPFURaBuFYlSCud6zX4_1-_OADbrk_VlnJxFJQ60y5GPkFdjlLTq0TdTl-D13XKJdd9S00lmHFKmrqOhiwl9e5JEaE86plq7MTqqzmrHQOlYcurQ_liojxT71UG5u_8qOl2ulu_veDt2DDG5zB1YxDtmHJjHZg_RsMob26n2O35rvQfszkNC8COz5zSN1BblVmpt1x1cBpQUfJYCg-B0NfwrkHz92bp-vb0PdVCBVBURFSpFNssGaaRCYSSqFUaGntAEUiYUxHKMMEwhFFUkvFmYiF6GgqTSo1j0VM9qExsu8_gMAuUlMthOLcWEK345Qz2aapMpiJtEOa0K52NVEedNz1vnhLarhkR4jEEiJxhEhwE87mQ8YzxI1FD7eqzU_8z5cn9c434bwiX337z8kOF092BGu45BcXf2lBo5hMzTGsqo9ikE9OSs77AiNL3eE priority: 102 providerName: ProQuest |
| Title | Robust monotone submodular function maximization |
| URI | https://link.springer.com/article/10.1007/s10107-018-1320-2 https://www.proquest.com/docview/2103985274 |
| Volume | 172 |
| WOSCitedRecordID | wos000447751100023&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: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1436-4646 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001388 issn: 0025-5610 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8NAEB60etCDb7E-Sg6elECy281ujiqKoC2l9X0J-woUrBWTij_f2TRpVVTQy8CSzSbMTOaR2fkWYN_QkBBUFl8RTjBB4dxXkWvwYZIqVBBBdAHiesnbbXF3F3fKPu6s2u1elSQLS_2h2S0stkli1uPaftHuzjEHNuNS9N7NxPyGVIjqnFYXHFSlzO-W-OyMphHml6Jo4WvOlv_1liuwVIaW3tFYF1Zhxj6tweIHwEEctSYordk6BN2hGmW5h5o4dJjcXobOcWjcxlTP-TsnM28g3_qDsllzA67PTq9Ozv3yBAVf0zDKfRaalFhiuKGRjaTWYSqNQo-vaSStbUptuQxJxEJllBZcxlI2DVM2VUbEMqabUHvC52-Bh6mhYUZKLYRFkQZxKrgKWKot4TJt0joEFSsTXcKLu1MuHpMpMLJjTYKsSRxrElKHg8ktz2Nsjd8m71byScrPLEuIK2QLhpl1HQ4reUwv_7jY9p9m78ACKQTqfrzsQi1_Gdk9mNeveT97acAsv71vwNzxabvTxdEF95G2ghNHScdR3kPaYQ-NQlPfAfnF2ik |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1JS8NAFH6UKqgHd7FaNQe9KMFm0mSmBxFxoaULIhV6i7MFCnaxaV3-lL_ReWnSqmBvPXgMSSbJvC9vmTfvewDHynUIMWCxBaHEBCiU2sLHAh-Pu8IAhBEZk7jWaKPBWq3SfQY-01oY3FaZ6sRYUauexDXyc4I5S-aZIOqy_2Jj1yjMrqYtNMawqOqPNxOyRReVGyPfE0LubpvXZTvpKmBL1_GHtueokGiiqHJ97XMpnZArYaygdH2udZFLTblDfM8RSkhGeYnzovKEDoViJY7kS0blLxRd5uMfVaX2RPM7LmNpi1j0S9Is6rhUz4k3eZqYDYuWyU87OHVuf-VjYzN3t_bfJmgdVhOH2roa_wEbkNHdTVj5RrNojuoTbtpoCwoPPTGKhpZ53x4ykVuRcQl6CrfjWmjlEalWh7-3O0mJ6jY8zuUDdiDbNc_fBctMqvIU55IxbYBcKIWMioIXSk0oD4tuDgqpFAOZkKpjb4_nYEoHjYIPjOADFHxAcnA6uaU_ZhSZdXE-FXaQKJcomEo6B2cpXKan_xxsb_ZgR7BUbtZrQa3SqO7DMomximtNecgOByN9AIvyddiOBocx6i14mjeKvgDgs0AN |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS8MwED9kiuiD3-J0ah98UsqapG3SR1GH4hxjfrC3kq_CwH2wduKfb9K12xQVxMemSVrurrm73t3vAM4UQRgbYXEFptg4KJS6IrQFPgEnwggIwzIHcW3SVot1u1G76HOaltnuZUhyWtNgUZoGWX2kkvpC4RvKUyaNB2RLgM0ZvOybEZvT1Xl8mR3FiDBW9my1hkIZ1vxui8-KaW5tfgmQ5nqnsfnvN96CjcLkdC6nMrINS3qwA-sLQITm6mGG3prugtcZikmaOUZChxar20mN0hwqm7DqWD1oeen0-XuvXxRx7sFz4-bp6tYtOiu4kqAwcwOkEqyxooqEOuRSooQrYSwBSUKutc-lphzhMEBCCckojzj3VSB0IhSLeET2oTIwzz8Ax7iMKlCcS8a0YbUXJYwKL0ikxpQnPqmCV5I1lgXsuO1-8RrPAZMtaWJDmtiSJsZVOJ8tGU0xN36bXCt5FRefXxpjG-BmgfG4q3BR8mZ--8fNDv80-xRW29eNuHnXuj-CNZzz1v6bqUElG0_0MazIt6yXjk9yofwAT0Petg |
| 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=Robust+monotone+submodular+function+maximization&rft.jtitle=Mathematical+programming&rft.au=Orlin%2C+James+B.&rft.au=Schulz%2C+Andreas+S.&rft.au=Udwani%2C+Rajan&rft.date=2018-11-01&rft.issn=0025-5610&rft.eissn=1436-4646&rft.volume=172&rft.issue=1-2&rft.spage=505&rft.epage=537&rft_id=info:doi/10.1007%2Fs10107-018-1320-2&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s10107_018_1320_2 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0025-5610&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0025-5610&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0025-5610&client=summon |