Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint

Constrained submodular maximization problems encompass a wide variety of applications, including personalized recommendation, team formation, and revenue maximization via viral marketing. The massive instances occurring in modern-day applications can render existing algorithms prohibitively slow. Mo...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:The Journal of artificial intelligence research Ročník 74; s. 661 - 690
Hlavní autoři: Amanatidis, Georgios, Fusco, Federico, Lazos, Philip, Leonardi, Stefano, Reiffenhäuser, Rebecca
Médium: Journal Article
Jazyk:angličtina
Vydáno: San Francisco AI Access Foundation 01.01.2022
Témata:
Approximation
Artificial intelligence
Constraints
Greedy algorithms
Mathematical analysis
Maximization
Optimization
ISSN:1076-9757, 1076-9757, 1943-5037
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract Constrained submodular maximization problems encompass a wide variety of applications, including personalized recommendation, team formation, and revenue maximization via viral marketing. The massive instances occurring in modern-day applications can render existing algorithms prohibitively slow. Moreover, frequently those instances are also inherently stochastic. Focusing on these challenges, we revisit the classic problem of maximizing a (possibly non-monotone) submodular function subject to a knapsack constraint. We present a simple randomized greedy algorithm that achieves a 5.83-approximation and runs in O(n log n) time, i.e., at least a factor n faster than other state-of-the-art algorithms. The versatility of our approach allows us to further transfer it to a stochastic version of the problem. There, we obtain a (9 + ε)-approximation to the best adaptive policy, which is the first constant approximation for non-monotone objectives. Experimental evaluation of our algorithms showcases their improved performance on real and synthetic data.
AbstractList Constrained submodular maximization problems encompass a wide variety of applications, including personalized recommendation, team formation, and revenue maximization via viral marketing. The massive instances occurring in modern-day applications can render existing algorithms prohibitively slow. Moreover, frequently those instances are also inherently stochastic. Focusing on these challenges, we revisit the classic problem of maximizing a (possibly non-monotone) submodular function subject to a knapsack constraint. We present a simple randomized greedy algorithm that achieves a 5.83-approximation and runs in O(n log n) time, i.e., at least a factor n faster than other state-of-the-art algorithms. The versatility of our approach allows us to further transfer it to a stochastic version of the problem. There, we obtain a (9 + ε)-approximation to the best adaptive policy, which is the first constant approximation for non-monotone objectives. Experimental evaluation of our algorithms showcases their improved performance on real and synthetic data.
Author Leonardi, Stefano
Amanatidis, Georgios
Reiffenhäuser, Rebecca
Fusco, Federico
Lazos, Philip
Author_xml – sequence: 1
  givenname: Georgios
  surname: Amanatidis
  fullname: Amanatidis, Georgios
– sequence: 2
  givenname: Federico
  surname: Fusco
  fullname: Fusco, Federico
– sequence: 3
  givenname: Philip
  surname: Lazos
  fullname: Lazos, Philip
– sequence: 4
  givenname: Stefano
  surname: Leonardi
  fullname: Leonardi, Stefano
– sequence: 5
  givenname: Rebecca
  surname: Reiffenhäuser
  fullname: Reiffenhäuser, Rebecca
BookMark eNptUMtOwzAQtFCRaAs3PsASV1L8yMvHqqKAoHAonK2N40gOrV1sBwFfT9JyQIjTrkYzs7MzQSPrrEbonJIZzSm_asH4GZ1RnhbsCI0pKfJEFFkx-rWfoEkILSFUpKwco_USQsTzGnbRvGv86GyyctbF3hivu2rr6m4DHq_gw2zNF0Tj7IC3WkUcHQZ8b2EXQL3ihbMhejA2nqLjBjZBn_3MKXpZXj8vbpOHp5u7xfwhUZzQmORFCWkDtMq50EzUWjUlyWitaF0KlpaFqCAHzSpOuOKlrrSoeVWlIPIe0ymfoouD7867t06HKFvXeduflCwvWSZYxrOexQ4s5V0IXjdSmbh_ZEi7kZTIoTw5lCep3JfXiy7_iHbebMF__k__BhTQdD4
CitedBy_id crossref_primary_10_1109_TWC_2021_3105906
crossref_primary_10_1145_3698397
crossref_primary_10_1109_TETCI_2023_3306362
crossref_primary_10_1016_j_orl_2025_107295
ContentType Journal Article
Copyright 2022. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about
Copyright_xml – notice: 2022. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about
DBID AAYXX
CITATION
8FE
8FG
ABUWG
AFKRA
ARAPS
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
GNUQQ
HCIFZ
JQ2
K7-
P62
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
DOI 10.1613/jair.1.13472
DatabaseName CrossRef
ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
Advanced Technologies & Aerospace Collection
ProQuest Central Essentials
ProQuest Central
Technology Collection
ProQuest One Community College
ProQuest Central
ProQuest Central Student
SciTech Premium Collection
ProQuest Computer Science Collection
Computer Science Database
ProQuest Advanced Technologies & Aerospace Collection
ProQuest Central Premium
ProQuest One Academic (New)
Publicly Available Content Database
ProQuest One Academic Middle East (New)
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
DatabaseTitle CrossRef
Publicly Available Content Database
Advanced Technologies & Aerospace Collection
Computer Science Database
ProQuest Central Student
Technology Collection
ProQuest One Academic Middle East (New)
ProQuest Advanced Technologies & Aerospace Collection
ProQuest Central Essentials
ProQuest Computer Science Collection
ProQuest One Academic Eastern Edition
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Technology Collection
ProQuest SciTech Collection
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
ProQuest One Academic UKI Edition
ProQuest Central Korea
ProQuest Central (New)
ProQuest One Academic
ProQuest One Academic (New)
DatabaseTitleList Publicly Available Content Database
CrossRef
Database_xml – sequence: 1
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISSN 1076-9757
1943-5037
EndPage 690
ExternalDocumentID 10_1613_jair_1_13472
GroupedDBID .DC
29J
2WC
5GY
5VS
AAKMM
AAKPC
AALFJ
AAYFX
AAYXX
ACGFO
ACM
ADBBV
ADBSK
ADMLS
AEFXT
AEJOY
AENEX
AFFHD
AFKRA
AFWXC
AKRVB
ALMA_UNASSIGNED_HOLDINGS
AMVHM
ARAPS
BCNDV
BENPR
BGLVJ
CCPQU
CITATION
E3Z
EBS
EJD
F5P
FRJ
FRP
GROUPED_DOAJ
GUFHI
HCIFZ
K7-
KQ8
LHSKQ
LPJ
OK1
OVT
P2P
PHGZM
PHGZT
PIMPY
PQGLB
RNS
TR2
XSB
8FE
8FG
ABUWG
AZQEC
DWQXO
GNUQQ
JQ2
P62
PKEHL
PQEST
PQQKQ
PQUKI
PRINS
PUEGO
ID FETCH-LOGICAL-c301t-678a4fa1b639e29decf8051dc1d8924879ba6ae2b303c38ebe9d3bb4a962b3e43
IEDL.DBID K7-
ISICitedReferencesCount 10
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000810515600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1076-9757
IngestDate Sat Sep 06 22:11:33 EDT 2025
Tue Nov 18 22:01:43 EST 2025
Sat Nov 29 05:27:06 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c301t-678a4fa1b639e29decf8051dc1d8924879ba6ae2b303c38ebe9d3bb4a962b3e43
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
OpenAccessLink https://www.proquest.com/docview/2682592535?pq-origsite=%requestingapplication%
PQID 2682592535
PQPubID 5160723
PageCount 30
ParticipantIDs proquest_journals_2682592535
crossref_citationtrail_10_1613_jair_1_13472
crossref_primary_10_1613_jair_1_13472
PublicationCentury 2000
PublicationDate 2022-01-01
PublicationDateYYYYMMDD 2022-01-01
PublicationDate_xml – month: 01
  year: 2022
  text: 2022-01-01
  day: 01
PublicationDecade 2020
PublicationPlace San Francisco
PublicationPlace_xml – name: San Francisco
PublicationTitle The Journal of artificial intelligence research
PublicationYear 2022
Publisher AI Access Foundation
Publisher_xml – name: AI Access Foundation
SSID ssj0019428
Score 2.432568
Snippet Constrained submodular maximization problems encompass a wide variety of applications, including personalized recommendation, team formation, and revenue...
SourceID proquest
crossref
SourceType Aggregation Database
Enrichment Source
Index Database
StartPage 661
SubjectTerms Approximation
Artificial intelligence
Constraints
Greedy algorithms
Mathematical analysis
Maximization
Optimization
Title Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint
URI https://www.proquest.com/docview/2682592535
Volume 74
WOSCitedRecordID wos000810515600001&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: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  customDbUrl:
  eissn: 1076-9757
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0019428
  issn: 1076-9757
  databaseCode: DOA
  dateStart: 19930101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVPQU
  databaseName: Computer Science Database
  customDbUrl:
  eissn: 1076-9757
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0019428
  issn: 1076-9757
  databaseCode: K7-
  dateStart: 19930101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/compscijour
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central - New (Subscription)
  customDbUrl:
  eissn: 1076-9757
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0019428
  issn: 1076-9757
  databaseCode: BENPR
  dateStart: 19930101
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  customDbUrl:
  eissn: 1076-9757
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0019428
  issn: 1076-9757
  databaseCode: PIMPY
  dateStart: 19930101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3JTsMwELXYDlzYEWvlA5yQC3EWxycEiAqEWlUsUjlF4yVSWZrSBMTnM07dIg5w4TqxpVGeZ8Yz9vgRcmAwwzG5BRbn6QmLIMwZhhHBpNF5Ik0YKDUmmxCdTtrrya4vuJX-WuXEJ9aO2hTa1ciPeYK5jORxGJ8O35hjjXKnq55CY5bMBxydsDuUFWx6iiAjPm6FEwmTIhb-4jtGsOMn6I-aQdM1UvKfIemnR67DTGv5vwqukCW_waRn4xWxSmbsYI0sT8gbqLfldXLXgrKiZwaGzuHRTjFgaN-Fe5ubojd5LYy7oErb8Nl_9b2aTu7KNrQqKNCbAQxL0M_UcX7WTBPVBnloXd5fXDHPsMA0GnbFMFJBlEOgcJ9iuTRWI2JxYHRgUkzMUiEVJGC5wkCnwxQBR_iUikAmKLNRuEnmBqjXFqFRkgJOU2HETWRjnQoIYyXyJASwqYy3ydHkJ2faPz_udHvJXBqCkGQOkizIaki2yeF09HD87MYv4_YmYGTe-MrsG4mdvz_vkkXuuhnqisoematG73afLOiPql-OGmT-_LLTvW3UaXqjXlko6163u49fKvvYcw
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VggQXylMUCvhAT8htYzuxfUCoAlattl0hUaTewvgRaYFulk14_Sl-I-M8inqAWw9cHScaZ758k7E9_gCeBcpwQhWR55XZ4wplxSmMaG6DrwobZOZcLzahZzNzemrfrsGvsRYmbascObEj6lD7NEe-KwrKZazIZf5y-YUn1ai0ujpKaPSwmMaf3ylla14cvib_bgsxeXPy6oAPqgLcE5hbTuyMqsLMUWyOwoboyco8Cz4LhpIRo63DAqNwRO5eGhokmeycQltQW1SSnnsFrippdPquppqfr1pYJfrSO11wq3M9bLSniLn7EeernWwnFW6KiyHwYgTowtpk4397Ibfg5vADzfZ7xN-Gtbi4AxujOAUbuOouvJtg07L9gMtE6GxWLzjxV53OHmfElmd1SBtw2TH-mJ8NtaipPU1LsbZmyKYLXDboP7GkadopabT34P2lDO0-rC_IrgfAVGGQbnNSiaBi7o1GmTtdFRIxGptvwvPRqaUfjldPtn0uU5pFECgTBMqs7CCwCdvnvZf9sSJ_6bc1Or8cyKUp_3j-4b8vP4XrByfHR-XR4Wz6CG6IVLnRzR5twXq7-hofwzX_rZ03qycdjhl8uGyc_AaQXTKY
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=Fast+Adaptive+Non-Monotone+Submodular+Maximization+Subject+to+a+Knapsack+Constraint&rft.jtitle=The+Journal+of+artificial+intelligence+research&rft.au=Amanatidis%2C+Georgios&rft.au=Fusco%2C+Federico&rft.au=Lazos%2C+Philip&rft.au=Leonardi%2C+Stefano&rft.date=2022-01-01&rft.pub=AI+Access+Foundation&rft.issn=1076-9757&rft.eissn=1943-5037&rft.volume=74&rft.spage=661&rft_id=info:doi/10.1613%2Fjair.1.13472
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1076-9757&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1076-9757&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1076-9757&client=summon