Practical Parallel Algorithms for Non-Monotone Submodular Maximization

Submodular maximization has found extensive applications in various domains within the field of artificial intelligence, including but not limited to machine learning, computer vision, and natural language processing. With the increasing size of datasets in these domains, there is a pressing need to...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:The Journal of artificial intelligence research Ročník 82; s. 39 - 75
Hlavní autoři: Cui, Shuang, Han, Kai, Tang, Jing, Li, Xueying, Zhiyuli, Aakas, Li, Hanxiao
Médium: Journal Article
Jazyk:angličtina
Vydáno: 06.01.2025
ISSN:1076-9757, 1076-9757
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract Submodular maximization has found extensive applications in various domains within the field of artificial intelligence, including but not limited to machine learning, computer vision, and natural language processing. With the increasing size of datasets in these domains, there is a pressing need to develop efficient and parallelizable algorithms for submodular maximization. One measure of the parallelizability of a submodular maximization algorithm is its adaptive complexity, which indicates the number of sequential rounds where a polynomial number of queries to the objective function can be executed in parallel. In this paper, we study the problem of non-monotone submodular maximization subject to a knapsack constraint, and propose a low-adaptivity algorithm achieving an (1/8 − ϵ)- approximation with practical Õ(n) query complexity. Moreover, we also propose the first algorithm with both provable approximation ratio and sublinear adaptive complexity for the problem of non-monotone submodular maximization subject to a k-system constraint. As a by-product, we show that our two algorithms can also be applied to the special case of submodular maximization subject to a cardinality constraint, and achieve performance bounds comparable with those of state-of-the-art algorithms. Finally, the effectiveness of our algorithms is demonstrated by extensive experiments on real-world applications.
AbstractList Submodular maximization has found extensive applications in various domains within the field of artificial intelligence, including but not limited to machine learning, computer vision, and natural language processing. With the increasing size of datasets in these domains, there is a pressing need to develop efficient and parallelizable algorithms for submodular maximization. One measure of the parallelizability of a submodular maximization algorithm is its adaptive complexity, which indicates the number of sequential rounds where a polynomial number of queries to the objective function can be executed in parallel. In this paper, we study the problem of non-monotone submodular maximization subject to a knapsack constraint, and propose a low-adaptivity algorithm achieving an (1/8 − ϵ)- approximation with practical Õ(n) query complexity. Moreover, we also propose the first algorithm with both provable approximation ratio and sublinear adaptive complexity for the problem of non-monotone submodular maximization subject to a k-system constraint. As a by-product, we show that our two algorithms can also be applied to the special case of submodular maximization subject to a cardinality constraint, and achieve performance bounds comparable with those of state-of-the-art algorithms. Finally, the effectiveness of our algorithms is demonstrated by extensive experiments on real-world applications.
Author Li, Xueying
Li, Hanxiao
Han, Kai
Tang, Jing
Zhiyuli, Aakas
Cui, Shuang
Author_xml – sequence: 1
  givenname: Shuang
  surname: Cui
  fullname: Cui, Shuang
– sequence: 2
  givenname: Kai
  surname: Han
  fullname: Han, Kai
– sequence: 3
  givenname: Jing
  surname: Tang
  fullname: Tang, Jing
– sequence: 4
  givenname: Xueying
  surname: Li
  fullname: Li, Xueying
– sequence: 5
  givenname: Aakas
  surname: Zhiyuli
  fullname: Zhiyuli, Aakas
– sequence: 6
  givenname: Hanxiao
  surname: Li
  fullname: Li, Hanxiao
BookMark eNpNkMtKAzEYhYNUsK3ufIA8gFPzJ2MysyzFqtAbqOuQifk1JTORZArq0ztVF27OZXMOfBMy6mLnCLkENgMJ4npvfJrBkCsGJ2QMTMmiVjdq9C-fkUnOe8agLnk1JstdMrb31gS6M8mE4AKdh9eYfP_WZoox0U3sinXsYj-c0cdD08aXQzCJrs2Hb_2X6X3szskpmpDdxZ9PyfPy9mlxX6y2dw-L-aqwXLC-EMhriVDWdhClnFVWMuASeQNVoxwbmmwaJThijchciSBAVYAGODIQU3L1u2tTzDk51O_JtyZ9amD6yEAfGWjQPwzEN7_nUfo
ContentType Journal Article
DBID AAYXX
CITATION
DOI 10.1613/jair.1.16801
DatabaseName CrossRef
DatabaseTitle CrossRef
DatabaseTitleList CrossRef
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISSN 1076-9757
EndPage 75
ExternalDocumentID 10_1613_jair_1_16801
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
ID FETCH-LOGICAL-c230t-3f296f149cf1477ec7c60126f2b18b7e06016bb732ff9ff0e4f131781fa12f013
ISICitedReferencesCount 0
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001400274500001&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 Nov 29 05:27:07 EST 2025
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Language English
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-c230t-3f296f149cf1477ec7c60126f2b18b7e06016bb732ff9ff0e4f131781fa12f013
OpenAccessLink https://jair.org/index.php/jair/article/download/16801/27115
PageCount 37
ParticipantIDs crossref_primary_10_1613_jair_1_16801
PublicationCentury 2000
PublicationDate 2025-01-06
PublicationDateYYYYMMDD 2025-01-06
PublicationDate_xml – month: 01
  year: 2025
  text: 2025-01-06
  day: 06
PublicationDecade 2020
PublicationTitle The Journal of artificial intelligence research
PublicationYear 2025
SSID ssj0019428
Score 2.431383
Snippet Submodular maximization has found extensive applications in various domains within the field of artificial intelligence, including but not limited to machine...
SourceID crossref
SourceType Index Database
StartPage 39
Title Practical Parallel Algorithms for Non-Monotone Submodular Maximization
Volume 82
WOSCitedRecordID wos001400274500001&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
  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/eLvHCXMwtV1LT8MwDI7G48CFN-KtHOCEOtZ2bZojQiCQYJrEkMapSroEKkGHyobGgf-O8-gD2GEcuERtVDdT7Dq2Z39G6KjFQzAiXOkkQQIOCg-kw6KEOa5qkiTAROfENJsgnU7U79Nuo_FZ1MK8P5MsiyYT-vqvrIY5YLYqnf0Du8uXwgRcA9NhBLbDOBPjDQKR2vouy1WnFODB8-MwT0dPBnzhpDPMHPiWhwqHW2mOl-FAJ6Peskn6Yusy60ZrVT6mDVe1psWdSOuAnhY3qIwvn491psDd05jZ41FruswkcaRVzMAmBafVUzeasj8WH8WkDUx4gQ5MhDVd2iKhQ4nBn26KKXNWAZvuQ1aDGmgjexabpiq_tDyYILq7QJo3XbiLbDjkG5j2j0OuTD1UTg_Qx4o6dmNNPYcWPBJQUIoL3evb7kP5NxRte6aW0v5qWzkB9Kf11Ws2Tc046a2iZcscfGakYQ01RLaOVoqOHdgq8A10WQoHLoQDV8KBQThwXThwJRy4Lhyb6P7yond-5dhOGk4CLubI8aVHQwnOcAIDISIhCTjiXig97kacCA3KwznxPSmplC3RlvCxksiVzPUkeAlbaD6DZbcRZgO_xSUYsXDRDkUYsTAY-MyVAfdpQPkOOi72In41gCnxtD3fnfG5PbRUCdc-mh_lY3GAFpP3UfqWH-oQyqFl2xe9vWT5
linkProvider ProQuest
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=Practical+Parallel+Algorithms+for+Non-Monotone+Submodular+Maximization&rft.jtitle=The+Journal+of+artificial+intelligence+research&rft.au=Cui%2C+Shuang&rft.au=Han%2C+Kai&rft.au=Tang%2C+Jing&rft.au=Li%2C+Xueying&rft.date=2025-01-06&rft.issn=1076-9757&rft.eissn=1076-9757&rft.volume=82&rft.spage=39&rft.epage=75&rft_id=info:doi/10.1613%2Fjair.1.16801&rft.externalDBID=n%2Fa&rft.externalDocID=10_1613_jair_1_16801
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