IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v43y2022i5d10.1007_s10878-020-00620-1.html
   My bibliography  Save this article

Maximizing a monotone non-submodular function under a knapsack constraint

Author

Listed:
  • Zhenning Zhang

    (Beijing University of Technology)

  • Bin Liu

    (Ocean University of China)

  • Yishui Wang

    (Chinese Academy of Sciences)

  • Dachuan Xu

    (Beijing University of Technology)

  • Dongmei Zhang

    (Shandong Jianzhu University)

Abstract

Submodular optimization has been well studied in combinatorial optimization. However, there are few works considering about non-submodular optimization problems which also have many applications, such as experimental design, some optimization problems in social networks, etc. In this paper, we consider the maximization of non-submodular function under a knapsack constraint, and explore the performance of the greedy algorithm, which is characterized by the submodularity ratio $$\beta $$ β and curvature $$\alpha $$ α . In particular, we prove that the greedy algorithm enjoys a tight approximation guarantee of $$ (1-e^{-\alpha \beta })/{\alpha }$$ ( 1 - e - α β ) / α for the above problem. To our knowledge, it is the first tight constant factor for this problem. We further utilize illustrative examples to demonstrate the performance of our algorithm.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:jcomop:v:43:y:2022:i:5:d:10.1007_s10878-020-00620-1
    DOI: 10.1007/s10878-020-00620-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-020-00620-1
    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/s10878-020-00620-1?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. Maxim Sviridenko & Jan Vondrák & Justin Ward, 2017. "Optimal Approximation for Submodular and Supermodular Optimization with Bounded Curvature," Mathematics of Operations Research, INFORMS, vol. 42(4), pages 1197-1218, November.
    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. 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).
    4. Akiyoshi Shioura & Natalia V. Shakhlevich & Vitaly A. Strusevich, 2016. "Application of Submodular Optimization to Single Machine Scheduling with Controllable Processing Times Subject to Release Dates and Deadlines," INFORMS Journal on Computing, INFORMS, vol. 28(1), pages 148-161, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kemin Yu & Min Li & Yang Zhou & Qian Liu, 2023. "On maximizing monotone or non-monotone k-submodular functions with the intersection of knapsack and matroid constraints," Journal of Combinatorial Optimization, Springer, vol. 45(3), pages 1-21, April.

    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. Bin Liu & Miaomiao Hu, 2022. "Fast algorithms for maximizing monotone nonsubmodular functions," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1655-1670, July.
    2. 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.
    3. 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.
    4. 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.
    5. Cheng Lu & Wenguo Yang & Ruiqi Yang & Suixiang Gao, 2022. "Maximizing a non-decreasing non-submodular function subject to various types of constraints," Journal of Global Optimization, Springer, vol. 83(4), pages 727-751, August.
    6. Sekar, Shreyas & Vojnovic, Milan & Yun, Se-Young, 2020. "A test score based approach to stochastic submodular optimization," LSE Research Online Documents on Economics 103176, London School of Economics and Political Science, LSE Library.
    7. Shreyas Sekar & Milan Vojnovic & Se-Young Yun, 2021. "A Test Score-Based Approach to Stochastic Submodular Optimization," Management Science, INFORMS, vol. 67(2), pages 1075-1092, February.
    8. Cheng Lu & Wenguo Yang & Suixiang Gao, 2022. "A new greedy strategy for maximizing monotone submodular function under a cardinality constraint," Journal of Global Optimization, Springer, vol. 83(2), pages 235-247, June.
    9. Zhenning Zhang & Bin Liu & Yishui Wang & Dachuan Xu & Dongmei Zhang, 0. "Maximizing a monotone non-submodular function under a knapsack constraint," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-24.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. repec:dgr:rugsom:99a17 is not listed on IDEAS
    15. Lehmann, Daniel, 2020. "Quality of local equilibria in discrete exchange economies," Journal of Mathematical Economics, Elsevier, vol. 88(C), pages 141-152.
    16. 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.
    17. Shengminjie Chen & Donglei Du & Wenguo Yang & Suixiang Gao, 2024. "Maximizing stochastic set function under a matroid constraint from decomposition," Journal of Combinatorial Optimization, Springer, vol. 48(1), pages 1-21, August.
    18. 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.
    19. 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.
    20. 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.
    21. 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.

    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:43:y:2022:i:5:d:10.1007_s10878-020-00620-1. 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.