Streaming Algorithms for Maximizing Monotone Submodular Functions Under a Knapsack Constraint

In this paper, we consider the problem of maximizing a monotone submodular function subject to a knapsack constraint in the streaming setting. In particular, the elements arrive sequentially and at any point of time, the algorithm has access only to a small fraction of the data stored in primary mem...

Full description

Saved in:

Bibliographic Details
Published in:	Algorithmica Vol. 82; no. 4; pp. 1006 - 1032
Main Authors:	Huang, Chien-Chung, Kakimura, Naonori, Yoshida, Yuichi
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.04.2020 Springer Nature B.V
Subjects:	Algorithm Analysis and Problem Complexity Algorithms Approximation Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Mathematical analysis Mathematics of Computing Maximization Optimization Theory of Computation Multiple-pass streaming Submodular functions Single-pass streaming Constant approximation
ISSN:	0178-4617, 1432-0541
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	In this paper, we consider the problem of maximizing a monotone submodular function subject to a knapsack constraint in the streaming setting. In particular, the elements arrive sequentially and at any point of time, the algorithm has access only to a small fraction of the data stored in primary memory. For this problem, we propose a ( 0.363 - ε ) -approximation algorithm, requiring only a single pass through the data; moreover, we propose a ( 0.4 - ε ) -approximation algorithm requiring a constant number of passes through the data. The required memory space of both algorithms depends only on the size of the knapsack capacity and ε .
AbstractList	In this paper, we consider the problem of maximizing a monotone submodular function subject to a knapsack constraint in the streaming setting. In particular, the elements arrive sequentially and at any point of time, the algorithm has access only to a small fraction of the data stored in primary memory. For this problem, we propose a (0.363-ε)-approximation algorithm, requiring only a single pass through the data; moreover, we propose a (0.4-ε)-approximation algorithm requiring a constant number of passes through the data. The required memory space of both algorithms depends only on the size of the knapsack capacity and ε. In this paper, we consider the problem of maximizing a monotone submodular function subject to a knapsack constraint in the streaming setting. In particular, the elements arrive sequentially and at any point of time, the algorithm has access only to a small fraction of the data stored in primary memory. For this problem, we propose a ( 0.363 - ε ) -approximation algorithm, requiring only a single pass through the data; moreover, we propose a ( 0.4 - ε ) -approximation algorithm requiring a constant number of passes through the data. The required memory space of both algorithms depends only on the size of the knapsack capacity and ε .
Author	Kakimura, Naonori Huang, Chien-Chung Yoshida, Yuichi
Author_xml	– sequence: 1 givenname: Chien-Chung surname: Huang fullname: Huang, Chien-Chung organization: DIENS, École Normale Supérieure, Université PSL – sequence: 2 givenname: Naonori orcidid: 0000-0002-3918-3479 surname: Kakimura fullname: Kakimura, Naonori email: kakimura@math.keio.ac.jp organization: Department of Mathematics, Keio University – sequence: 3 givenname: Yuichi surname: Yoshida fullname: Yoshida, Yuichi organization: National Institute of Informatics
BookMark	eNp9kEFPwzAMhSMEEtvgD3CKxLlgp23SHqeJAWITh7EjirI2HR1rMpJUYvx6WoaExGEnS_b77Oc3JKfGGk3IFcINAohbD5CkcQSYRwCcZdH-hAwwiVkEaYKnZAAosijhKM7J0PsNADKR8wF5XQSnVVObNR1v19bV4a3xtLKOztVn3dRf_WRujQ3dQbpoV40t261ydNqaItTWeLo0pXZU0Sejdl4V73TSdYNTtQkX5KxSW68vf-uILKd3L5OHaPZ8_zgZz6IixjxEmRacc13mFU-xSLBUpaoYjwtExVJM1SorE8ZECkojaChFIUT_guYAWqt4RK4Pe3fOfrTaB7mxrTPdSclinmCc5yLvVNlBVTjrvdOVLOqg-id6t1uJIPsw5SFM2YUpf8KU-w5l_9Cdqxvl9seh-AD5TmzW2v25OkJ9AxWxitQ
CitedBy_id	crossref_primary_10_1007_s00224_021_10065_6 crossref_primary_10_1007_s11432_020_3420_9 crossref_primary_10_1145_3543516_3453922 crossref_primary_10_26599_TST_2022_9010068 crossref_primary_10_1007_s10878_022_00858_x crossref_primary_10_26599_TST_2022_9010033 crossref_primary_10_1007_s11704_024_40266_4 crossref_primary_10_1145_3447383 crossref_primary_10_26599_TST_2023_9010121 crossref_primary_10_1142_S0217595922500130 crossref_primary_10_1137_20M1357317 crossref_primary_10_1145_3570615
Cites_doi	10.1137/130920277 10.1145/2187836.2187888 10.1137/1.9781611973402.110 10.1145/956750.956769 10.1007/s10107-015-0900-7 10.1287/moor.1100.0463 10.1016/S0167-6377(03)00062-2 10.1007/BFb0121195 10.1145/2623330.2623637 10.1137/080733991 10.1137/110839655 10.1287/moor.7.3.410 10.1137/1.9781611974782.78 10.1007/978-3-662-47672-7_26
ContentType	Journal Article
Copyright	Springer Science+Business Media, LLC, part of Springer Nature 2019 2019© Springer Science+Business Media, LLC, part of Springer Nature 2019
Copyright_xml	– notice: Springer Science+Business Media, LLC, part of Springer Nature 2019 – notice: 2019© Springer Science+Business Media, LLC, part of Springer Nature 2019
DBID	AAYXX CITATION JQ2
DOI	10.1007/s00453-019-00628-y
DatabaseName	CrossRef ProQuest Computer Science Collection
DatabaseTitle	CrossRef ProQuest Computer Science Collection
DatabaseTitleList	ProQuest Computer Science Collection
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1432-0541
EndPage	1032
ExternalDocumentID	10_1007_s00453_019_00628_y
GrantInformation_xml	– fundername: Japan Society for the Promotion of Science grantid: JP18H05291 funderid: http://dx.doi.org/10.13039/501100001691 – fundername: Japan Society for the Promotion of Science grantid: JP17K00028; JP17H04676 funderid: http://dx.doi.org/10.13039/501100001691 – fundername: Exploratory Research for Advanced Technology grantid: JPMJER1201 funderid: http://dx.doi.org/10.13039/501100009024
GroupedDBID	-4Z -59 -5G -BR -EM -Y2 -~C -~X .86 .DC .VR 06D 0R~ 0VY 199 1N0 1SB 203 23M 28- 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 78A 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDPE ABDZT ABECU ABFSI ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTAH ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AI. AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. BA0 BBWZM BDATZ BGNMA BSONS CAG COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP E.L EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ H~9 I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P9O PF- PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TN5 TSG TSK TSV TUC U2A UG4 UOJIU UQL UTJUX UZXMN VC2 VFIZW VH1 VXZ W23 W48 WK8 YLTOR Z45 Z7X Z83 Z88 Z8R Z8W Z92 ZMTXR ZY4 ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION JQ2
ID	FETCH-LOGICAL-c319t-8e7666ed9f651c41dadaf263c11a2515ab8d422750ae10e0d7c770012e600eea3
IEDL.DBID	RSV
ISICitedReferencesCount	20
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001027952200013&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0178-4617
IngestDate	Thu Oct 02 16:35:05 EDT 2025 Tue Nov 18 22:13:00 EST 2025 Sat Nov 29 02:20:29 EST 2025 Fri Feb 21 02:43:05 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	4
Keywords	Multiple-pass streaming Submodular functions Single-pass streaming Constant approximation
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c319t-8e7666ed9f651c41dadaf263c11a2515ab8d422750ae10e0d7c770012e600eea3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-3918-3479
PQID	2364139979
PQPubID	2043795
PageCount	27
ParticipantIDs	proquest_journals_2364139979 crossref_citationtrail_10_1007_s00453_019_00628_y crossref_primary_10_1007_s00453_019_00628_y springer_journals_10_1007_s00453_019_00628_y
PublicationCentury	2000
PublicationDate	2020-04-01
PublicationDateYYYYMMDD	2020-04-01
PublicationDate_xml	– month: 04 year: 2020 text: 2020-04-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	Algorithmica
PublicationTitleAbbrev	Algorithmica
PublicationYear	2020
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	Badanidiyuru, A., Mirzasoleiman, B., Karbasi, A., Krause, A.: Streaming submodular maximization: massive data summarization on the fly. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 671–680 (2014) FilmusYWardJA tight combinatorial algorithm for submodular maximization subject to a matroid constraintSIAM J. Comput.2014432514542318305010.1137/130920277 Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 137–146 (2003) Kulik, A., Shachnai, H., Tamir, T.: Maximizing submodular set functions subject to multiple linear constraints. In: Proceedings of the 20th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 545–554 (2013) CalinescuGChekuriCPálMVondrákJMaximizing a monotone submodular function subject to a matroid constraintSIAM J. Comput.201140617401766286319310.1137/080733991 LeeJSviridenkoMVondrákJSubmodular maximization over multiple matroids via generalized exchange propertiesMath. Oper. Res.2010354795806277751510.1287/moor.1100.0463 Soma, T., Kakimura, N., Inaba, K., Kawarabayashi, K.: Optimal budget allocation: theoretical guarantee and efficient algorithm. In: Proceedings of the 31st International Conference on Machine Learning (ICML), pp. 351–359 (2014) WolseyLMaximising real-valued submodular functions: primal and dual heuristics for location problemsMath. Oper. Res.1982741042566793210.1287/moor.7.3.410 KrauseASinghAPGuestrinCNear-optimal sensor placements in gaussian processes: theory, efficient algorithms and empirical studiesJ. Mach. Learn. Res.200892352841225.68192 Yu, Q., Xu, E.L., Cui, S.: Streaming algorithms for news and scientific literature recommendation: submodular maximization with a d\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}-knapsack constraint. In: IEEE Global Conference on Signal and Information Processing (2016) Badanidiyuru, A., Vondrák, J.: Fast algorithms for maximizing submodular functions. In: Proceedings of the 25th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 1497–1514 (2013) Lin, H., Bilmes, J.: Multi-document summarization via budgeted maximization of submodular functions. In: Proceedings of the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp. 912–920 (2010) ChakrabartiAKaleSSubmodular maximization meets streaming: matchings, matroids, and moreMath. Program.20151541–2225247342193410.1007/s10107-015-0900-7 FisherMLNemhauserGLWolseyLAAn analysis of approximations for maximizing submodular set functions IIMath. Program. Study19788738751036910.1007/BFb0121195 Lin, H., Bilmes, J.: A class of submodular functions for document summarization. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), pp. 510–520 (2011) LeeJMaximum Entropy Sampling, Encyclopedia of Environmetrics2006New YorkWiley12291234 Alon, N., Gamzu, I., Tennenholtz, M.: Optimizing budget allocation among channels and influencers. In: Proceedings of the 21st International Conference on World Wide Web (WWW), pp. 381–388 (2012) Chan, T.H.H., Huang, Z., Jiang, S.H.C., Kang, N., Tang, Z.G.: Online submodular maximization with free disposal: Randomization beats for partition matroids online. In: Proceedings of the 28th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 1204–1223 (2017) ChekuriChandraGuptaShalmoliQuanrudKentStreaming Algorithms for Submodular Function MaximizationAutomata, Languages, and Programming2015Berlin, HeidelbergSpringer Berlin Heidelberg31833010.1007/978-3-662-47672-7_26 ChekuriCVondrákJZenklusenRSubmodular function maximization via the multilinear relaxation and contention resolution schemesSIAM J. Comput.201443618311879328128710.1137/110839655 SviridenkoMA note on maximizing a submodular set function subject to a knapsack constraintOper. Res. Lett.20043214143201710710.1016/S0167-6377(03)00062-2 628_CR6 G Calinescu (628_CR4) 2011; 40 A Krause (628_CR12) 2008; 9 628_CR2 628_CR3 J Lee (628_CR14) 2006 L Wolsey (628_CR20) 1982; 7 Chandra Chekuri (628_CR7) 2015 ML Fisher (628_CR10) 1978; 8 C Chekuri (628_CR8) 2014; 43 628_CR21 628_CR11 628_CR13 M Sviridenko (628_CR19) 2004; 32 628_CR16 628_CR17 Y Filmus (628_CR9) 2014; 43 628_CR18 628_CR1 A Chakrabarti (628_CR5) 2015; 154 J Lee (628_CR15) 2010; 35
References_xml	– reference: Badanidiyuru, A., Mirzasoleiman, B., Karbasi, A., Krause, A.: Streaming submodular maximization: massive data summarization on the fly. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 671–680 (2014) – reference: Lin, H., Bilmes, J.: Multi-document summarization via budgeted maximization of submodular functions. In: Proceedings of the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp. 912–920 (2010) – reference: SviridenkoMA note on maximizing a submodular set function subject to a knapsack constraintOper. Res. Lett.20043214143201710710.1016/S0167-6377(03)00062-2 – reference: LeeJSviridenkoMVondrákJSubmodular maximization over multiple matroids via generalized exchange propertiesMath. Oper. Res.2010354795806277751510.1287/moor.1100.0463 – reference: Badanidiyuru, A., Vondrák, J.: Fast algorithms for maximizing submodular functions. In: Proceedings of the 25th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 1497–1514 (2013) – reference: KrauseASinghAPGuestrinCNear-optimal sensor placements in gaussian processes: theory, efficient algorithms and empirical studiesJ. Mach. Learn. Res.200892352841225.68192 – reference: ChekuriCVondrákJZenklusenRSubmodular function maximization via the multilinear relaxation and contention resolution schemesSIAM J. Comput.201443618311879328128710.1137/110839655 – reference: Lin, H., Bilmes, J.: A class of submodular functions for document summarization. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), pp. 510–520 (2011) – reference: CalinescuGChekuriCPálMVondrákJMaximizing a monotone submodular function subject to a matroid constraintSIAM J. Comput.201140617401766286319310.1137/080733991 – reference: Chan, T.H.H., Huang, Z., Jiang, S.H.C., Kang, N., Tang, Z.G.: Online submodular maximization with free disposal: Randomization beats for partition matroids online. In: Proceedings of the 28th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 1204–1223 (2017) – reference: Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 137–146 (2003) – reference: Soma, T., Kakimura, N., Inaba, K., Kawarabayashi, K.: Optimal budget allocation: theoretical guarantee and efficient algorithm. In: Proceedings of the 31st International Conference on Machine Learning (ICML), pp. 351–359 (2014) – reference: ChakrabartiAKaleSSubmodular maximization meets streaming: matchings, matroids, and moreMath. Program.20151541–2225247342193410.1007/s10107-015-0900-7 – reference: WolseyLMaximising real-valued submodular functions: primal and dual heuristics for location problemsMath. Oper. Res.1982741042566793210.1287/moor.7.3.410 – reference: Yu, Q., Xu, E.L., Cui, S.: Streaming algorithms for news and scientific literature recommendation: submodular maximization with a d\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}-knapsack constraint. In: IEEE Global Conference on Signal and Information Processing (2016) – reference: LeeJMaximum Entropy Sampling, Encyclopedia of Environmetrics2006New YorkWiley12291234 – reference: ChekuriChandraGuptaShalmoliQuanrudKentStreaming Algorithms for Submodular Function MaximizationAutomata, Languages, and Programming2015Berlin, HeidelbergSpringer Berlin Heidelberg31833010.1007/978-3-662-47672-7_26 – reference: Kulik, A., Shachnai, H., Tamir, T.: Maximizing submodular set functions subject to multiple linear constraints. In: Proceedings of the 20th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 545–554 (2013) – reference: FilmusYWardJA tight combinatorial algorithm for submodular maximization subject to a matroid constraintSIAM J. Comput.2014432514542318305010.1137/130920277 – reference: FisherMLNemhauserGLWolseyLAAn analysis of approximations for maximizing submodular set functions IIMath. Program. Study19788738751036910.1007/BFb0121195 – reference: Alon, N., Gamzu, I., Tennenholtz, M.: Optimizing budget allocation among channels and influencers. In: Proceedings of the 21st International Conference on World Wide Web (WWW), pp. 381–388 (2012) – volume: 9 start-page: 235 year: 2008 ident: 628_CR12 publication-title: J. Mach. Learn. Res. – volume: 43 start-page: 514 issue: 2 year: 2014 ident: 628_CR9 publication-title: SIAM J. Comput. doi: 10.1137/130920277 – ident: 628_CR1 doi: 10.1145/2187836.2187888 – ident: 628_CR3 doi: 10.1137/1.9781611973402.110 – ident: 628_CR11 doi: 10.1145/956750.956769 – volume: 154 start-page: 225 issue: 1–2 year: 2015 ident: 628_CR5 publication-title: Math. Program. doi: 10.1007/s10107-015-0900-7 – volume: 35 start-page: 795 issue: 4 year: 2010 ident: 628_CR15 publication-title: Math. Oper. Res. doi: 10.1287/moor.1100.0463 – volume: 32 start-page: 41 issue: 1 year: 2004 ident: 628_CR19 publication-title: Oper. Res. Lett. doi: 10.1016/S0167-6377(03)00062-2 – volume: 8 start-page: 73 year: 1978 ident: 628_CR10 publication-title: Math. Program. Study doi: 10.1007/BFb0121195 – ident: 628_CR21 – ident: 628_CR16 – start-page: 1229 volume-title: Maximum Entropy Sampling, Encyclopedia of Environmetrics year: 2006 ident: 628_CR14 – ident: 628_CR2 doi: 10.1145/2623330.2623637 – volume: 40 start-page: 1740 issue: 6 year: 2011 ident: 628_CR4 publication-title: SIAM J. Comput. doi: 10.1137/080733991 – ident: 628_CR17 – ident: 628_CR18 – volume: 43 start-page: 1831 issue: 6 year: 2014 ident: 628_CR8 publication-title: SIAM J. Comput. doi: 10.1137/110839655 – ident: 628_CR13 – volume: 7 start-page: 410 year: 1982 ident: 628_CR20 publication-title: Math. Oper. Res. doi: 10.1287/moor.7.3.410 – ident: 628_CR6 doi: 10.1137/1.9781611974782.78 – start-page: 318 volume-title: Automata, Languages, and Programming year: 2015 ident: 628_CR7 doi: 10.1007/978-3-662-47672-7_26
SSID	ssj0012796
Score	2.3493693
Snippet	In this paper, we consider the problem of maximizing a monotone submodular function subject to a knapsack constraint in the streaming setting. In particular,...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	1006
SubjectTerms	Algorithm Analysis and Problem Complexity Algorithms Approximation Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Mathematical analysis Mathematics of Computing Maximization Optimization Theory of Computation
Title	Streaming Algorithms for Maximizing Monotone Submodular Functions Under a Knapsack Constraint
URI	https://link.springer.com/article/10.1007/s00453-019-00628-y https://www.proquest.com/docview/2364139979
Volume	82
WOSCitedRecordID	wos001027952200013&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 customDbUrl: eissn: 1432-0541 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0012796 issn: 0178-4617 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/eLvHCXMwnV3NS8MwFA86PXhxfuJ0Sg7eNLA27ZoehzgEdYhf7CLlLUm1uG5jreL8633J2g1FBT0nTcNLXt7v8T5-hByi0cSzBck49GLmcYiZ4EoxVwsV-kpLHtvu-hdBpyO63fCqKArLymz3MiRpX-pZsZtBHyb3J2S28I9NFskSmjth1PH65n4WO3ADy8pleOeZhwa6KJX5fo3P5miOMb-ERa21aVf_t881slqgS9qaXod1sqAHG6RaMjfQQpE3yYMJRkOKy9JW_3E4TvKnNKOIX-klvCVp8m5GUN2HplU3xcclHSqTr0rbaAbtTaWWMIkCPR_AKAP5TA31pyWcyLfIXfv09uSMFUQLTKIG5kzoAL0YrcK46TvScxQoiN0ml44DiH986AnluaYTPGinoRsqkIGJV7sa4ZLWwLdJZYD72SEU4YMOJHAOPngeCAFuiC6M7_fQU8P_1IhTyjuSRRdys7d-NOufbOUXofwiK79oUiNHs29G0x4cv86ul8cYFfqYRaZNPmLdMAhr5Lg8tvnwz6vt_m36HllxjUNuU3vqpJKPX_Q-WZaveZKND-w9_QCjvOMM
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1ZSwMxEB68QF-sJ1ar5sE3DXQvd_dRxFLpgWiVvsgyTbJa7EV3FfXXO0l3WxQV9DnZbJhkMt8wxwdwREaTzhYFd7ATc9fBmAeOlNxWgQw9qYQTm-76db_ZDNrt8CorCkvybPc8JGle6mmxm0YfOvcn5Kbwj7_Nw6JLFksn8l3f3E1jB7ZvWLk07zx3yUBnpTLfr_HZHM0w5pewqLE2lcL_9rkGqxm6ZGeT67AOc2qwAYWcuYFlirwJ9zoYjX1alp31HobjbvrYTxjhV9bA126_-65HSN2HulU3o8elP5Q6X5VVyAyam8oMYRJDVhvgKEHxxDT1pyGcSLfgtnLROq_yjGiBC9LAlAfKJy9GyTA-9SzhWhIlxvapIywLCf942Amka-tO8KissipLX_g6Xm0rgktKobMNCwPazw4wgg_KF-g46KHrYhCgHZIL43kd8tToP0WwcnlHIutCrvfWi6b9k438IpJfZOQXvRXhePrNaNKD49fZpfwYo0wfk0i3ySesG_phEU7yY5sN_7za7t-mH8JytdWoR_XLZm0PVmztnJs0nxIspONntQ9L4iXtJuMDc2c_AFEK5fA
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LSwMxEB60inixPrFaNQdvGuq-urvHohalWgo-8CLLNMlqsS_aVay_3km626qoIJ43m4S85htm5vsA9slo0t6i4A42Y-46GPPAkZLbKpChJ5VwYsOuf-HX68HdXdj4UMVvst2zkOS4pkGzNHWTUl_GpUnhm0YiOg8o5KYIkI9mYc7VokHaX7-6ncQRbN8odGkNeu6SsU7LZr7v47NpmuLNLyFSY3mq-f_PeRmWUtTJKuNjsgIzqrsK-UzRgaUXfA3udZAaOzQEq7QfeoNW8tgZMsK17BJfW53Wm_5Cz0BPU3gzenQ6PanzWFmVzKM5wcwIKTFktS72hyiemJYENUIUyTrcVE-vj894KsDABd3MhAfKJ-9GyTAue5ZwLYkSY7vsCMtCwkUeNgPp2pohHpV1pI6kL3wdx7YVwSil0NmAXJfmswmMYIXyBToOeui6GARoh-TaeF6TPDgapwBWtvaRSNnJ9dza0YRX2axfROsXmfWLRgU4mPzTH3Nz_Nq6mG1plN7TYaTp8wkDh35YgMNsC6eff-5t62_N92ChcVKNLs7rtW1YtLXPbrJ_ipBLBs9qB-bFS9IaDnbN8X0HQX3u1A
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=Streaming+Algorithms+for+Maximizing+Monotone+Submodular+Functions+Under+a+Knapsack+Constraint&rft.jtitle=Algorithmica&rft.au=Huang%2C+Chien-Chung&rft.au=Kakimura%2C+Naonori&rft.au=Yoshida%2C+Yuichi&rft.date=2020-04-01&rft.issn=0178-4617&rft.eissn=1432-0541&rft.volume=82&rft.issue=4&rft.spage=1006&rft.epage=1032&rft_id=info:doi/10.1007%2Fs00453-019-00628-y&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s00453_019_00628_y
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0178-4617&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0178-4617&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0178-4617&client=summon