IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v41y2021i1d10.1007_s10878-020-00662-5.html
   My bibliography  Save this article

Sequence submodular maximization meets streaming

Author

Listed:
  • Ruiqi Yang

    (Beijing University of Technology
    University of Chinese Academy Sciences)

  • Dachuan Xu

    (Beijing University of Technology)

  • Longkun Guo

    (Qilu University of Technology (Shandong Academy of Sciences))

  • Dongmei Zhang

    (Shandong Jianzhu University)

Abstract

In this paper, we study the problem of maximizing a sequence submodular function in the streaming setting, where the utility function is defined on sequences instead of sets of elements. We encode the sequence submodular maximization with a weighted digraph, in which the weight of a vertex reveals the utility value in selecting a single element and the weight of an edge reveals the additional profit with respect to a certain selection sequence. The edges are visited in a streaming fashion and the aim is to sieve a sequence of at most k elements from the stream, such that the utility is maximized. In this work, we present an edge-based threshold procedure, which makes one pass over the stream, attains an approximation ratio of $$(1/(2\varDelta +1)- O(\epsilon ))$$ ( 1 / ( 2 Δ + 1 ) - O ( ϵ ) ) , consumes $$O(k\varDelta /\epsilon )$$ O ( k Δ / ϵ ) memory source in total and $$O(\log (k\varDelta )/\epsilon )$$ O ( log ( k Δ ) / ϵ ) update time per edge, where $$\varDelta $$ Δ is the minimum of the maximal outdegree and indegree of the directed graph.

Suggested Citation

  • Ruiqi Yang & Dachuan Xu & Longkun Guo & Dongmei Zhang, 2021. "Sequence submodular maximization meets streaming," Journal of Combinatorial Optimization, Springer, vol. 41(1), pages 43-55, January.
    • Handle: RePEc:spr:jcomop:v:41:y:2021:i:1:d:10.1007_s10878-020-00662-5
    DOI: 10.1007/s10878-020-00662-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-020-00662-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10878-020-00662-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zengfu Wang & Bill Moran & Xuezhi Wang & Quan Pan, 2016. "Approximation for maximizing monotone non-decreasing set functions with a greedy method," Journal of Combinatorial Optimization, Springer, vol. 31(1), pages 29-43, January.
    2. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions," LIDAM Reprints CORE 341, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Yijing Wang & Dachuan Xu & Yishui Wang & Dongmei Zhang, 2020. "Non-submodular maximization on massive data streams," Journal of Global Optimization, Springer, vol. 76(4), pages 729-743, April.
    4. Ruiqi Yang & Dachuan Xu & Yanjun Jiang & Yishui Wang & Dongmei Zhang, 2019. "Approximating Robust Parameterized Submodular Function Maximization in Large-Scales," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(04), pages 1-24, August.
    5. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions - 1," LIDAM Reprints CORE 334, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chenggang Wang & Zengfu Wang & Xiong Xu & Yuhang Hao, 2021. "A balanced sensor scheduling for multitarget localization in a distributed multiple-input multiple-output radar network," International Journal of Distributed Sensor Networks, , vol. 17(7), pages 15501477211, July.
    2. Majun Shi & Zishen Yang & Wei Wang, 2023. "Greedy Guarantees for Non-submodular Function Maximization Under Independent System Constraint with Applications," Journal of Optimization Theory and Applications, Springer, vol. 196(2), pages 516-543, February.
    3. Bin Liu & Zihan Chen & Huijuan Wang & Weili Wu, 2023. "An optimal streaming algorithm for non-submodular functions maximization on the integer lattice," Journal of Combinatorial Optimization, Springer, vol. 45(1), pages 1-17, January.
    4. Dam, Tien Thanh & Ta, Thuy Anh & Mai, Tien, 2022. "Submodularity and local search approaches for maximum capture problems under generalized extreme value models," European Journal of Operational Research, Elsevier, vol. 300(3), pages 953-965.
    5. Beck, Yasmine & Ljubić, Ivana & Schmidt, Martin, 2023. "A survey on bilevel optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 311(2), pages 401-426.
    6. Rad Niazadeh & Negin Golrezaei & Joshua Wang & Fransisca Susan & Ashwinkumar Badanidiyuru, 2023. "Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization," Management Science, INFORMS, vol. 69(7), pages 3797-3817, July.
    7. Alexandre D. Jesus & Luís Paquete & Arnaud Liefooghe, 2021. "A model of anytime algorithm performance for bi-objective optimization," Journal of Global Optimization, Springer, vol. 79(2), pages 329-350, February.
    8. Bin Liu & Miaomiao Hu, 2022. "Fast algorithms for maximizing monotone nonsubmodular functions," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1655-1670, July.
    9. repec:dgr:rugsom:99a17 is not listed on IDEAS
    10. Lehmann, Daniel, 2020. "Quality of local equilibria in discrete exchange economies," Journal of Mathematical Economics, Elsevier, vol. 88(C), pages 141-152.
    11. Eric DuBois & Ashley Peper & Laura A. Albert, 2023. "Interdicting Attack Plans with Boundedly Rational Players and Multiple Attackers: An Adversarial Risk Analysis Approach," Decision Analysis, INFORMS, vol. 20(3), pages 202-219, September.
    12. Zhenning Zhang & Bin Liu & Yishui Wang & Dachuan Xu & Dongmei Zhang, 2022. "Maximizing a monotone non-submodular function under a knapsack constraint," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1125-1148, July.
    13. Zhenning Zhang & Donglei Du & Yanjun Jiang & Chenchen Wu, 2021. "Maximizing DR-submodular+supermodular functions on the integer lattice subject to a cardinality constraint," Journal of Global Optimization, Springer, vol. 80(3), pages 595-616, July.
    14. Awi Federgruen & Nan Yang, 2008. "Selecting a Portfolio of Suppliers Under Demand and Supply Risks," Operations Research, INFORMS, vol. 56(4), pages 916-936, August.
    15. Yingfei Wang & Inbal Yahav & Balaji Padmanabhan, 2024. "Smart Testing with Vaccination: A Bandit Algorithm for Active Sampling for Managing COVID-19," Information Systems Research, INFORMS, vol. 35(1), pages 120-144, March.
    16. Yanzhi Li & Zhicheng Liu & Chuchu Xu & Ping Li & Xiaoyan Zhang & Hong Chang, 2023. "Two-stage submodular maximization under curvature," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-16, March.
    17. Simon Bruggmann & Rico Zenklusen, 2019. "Submodular Maximization Through the Lens of Linear Programming," Management Science, INFORMS, vol. 44(4), pages 1221-1244, November.
    18. Xiaojuan Zhang & Qian Liu & Min Li & Yang Zhou, 2022. "Fast algorithms for supermodular and non-supermodular minimization via bi-criteria strategy," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3549-3574, December.
    19. Hans Kellerer & Vitaly A. Strusevich, 2016. "Optimizing the half-product and related quadratic Boolean functions: approximation and scheduling applications," Annals of Operations Research, Springer, vol. 240(1), pages 39-94, May.
    20. Hyoshin (John) Park & Ali Haghani & Song Gao & Michael A. Knodler & Siby Samuel, 2018. "Anticipatory Dynamic Traffic Sensor Location Problems with Connected Vehicle Technologies," Service Science, INFORMS, vol. 52(6), pages 1299-1326, December.
    21. Shaojie Tang & Jing Yuan, 2023. "Beyond submodularity: a unified framework of randomized set selection with group fairness constraints," Journal of Combinatorial Optimization, Springer, vol. 45(4), pages 1-22, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v:41:y:2021:i:1:d:10.1007_s10878-020-00662-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.