IDEAS home Printed from https://ideas.repec.org/p/has/discpr/1724.html
   My bibliography  Save this paper

Matchings with lower quotas: Algorithms and complexity

Author

Listed:
  • Ashwin Arulselvan

    (Department of Management Science, Sir William Duncan Building, University of Strathclyde)

  • Agnes Cseh

    (Institute of Economics, Research Centre for Economic and Regional Studies, Hungarian Academy of Sciences, and Corvinus University of Budapest)

  • Martin Groß

    (Institute for Mathematics, Technische Universität Berlin)

  • David F. Manlove

    (School of Computing Science, Sir Alwyn Williams Building, University of Glasgow)

  • Jannik Matuschke

    (TUM School of Management, Technische Universiät München)

Abstract

We study a natural generalization of the maximum weight many-to- one matching problem. We are given an undirected bipartite graph G = (A[_ P;E) with weights on the edges in E, and with lower and upper quotas on the vertices in P.We seek a maximum weight many-to-one matching satisfying two sets of constraints: vertices in A are incident to at most one matching edge, while vertices in P are either unmatched or they are incident to a number of matching edges between their lower and upper quota. This problem, which we call maximum weight many-to-one matching with lower and upper quotas (wmlq), has applications to the assignment of students to projects within university courses, where there are constraints on the minimum and maximum numbers of students that must be assigned to each project. In this paper, we provide a comprehensive analysis of the complexity of wmlq from the viewpoints of classical polynomial time algorithms, xedparameter tractability, as well as approximability. We draw the line between NP-hard and polynomially tractable instances in terms of degree and quota constraints and provide ecient algorithms to solve the tractable ones. We further show that the problem can be solved in polynomial time for instances with bounded treewidth; however, the corresponding runtime is exponential in the treewidth with the maximum upper quota umax as basis, and we prove that this dependence is necessary unless FPT = W[1]. The approximability of wmlq is also discussed: we present an approximation algorithm for the general case with performance guarantee umax + 1, which is asymptotically best possible unless P = NP. Finally, we elaborate on how most of our positive results carry over to matchings in arbitrary graphs with lower quotas.

Suggested Citation

  • Ashwin Arulselvan & Agnes Cseh & Martin Groß & David F. Manlove & Jannik Matuschke, 2017. "Matchings with lower quotas: Algorithms and complexity," CERS-IE WORKING PAPERS 1724, Institute of Economics, Centre for Economic and Regional Studies.
  • Handle: RePEc:has:discpr:1724
    as

    Download full text from publisher

    File URL: http://econ.core.hu/file/download/mtdp/MTDP1724.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuichiro Kamada & Fuhito Kojima, 2012. "Stability and Strategy-Proofness for Matching with Constraints: A Problem in the Japanese Medical Match and Its Solution," American Economic Review, American Economic Association, vol. 102(3), pages 366-370, May.
    2. Monte, Daniel & Tumennasan, Norovsambuu, 2013. "Matching with quorums," Economics Letters, Elsevier, vol. 120(1), pages 14-17.
    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. Scott Duke Kominers & Alexander Teytelboym & Vincent P Crawford, 2017. "An invitation to market design," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 33(4), pages 541-571.
    2. Kadam, Sangram Vilasrao, 2017. "Unilateral substitutability implies substitutable completability in many-to-one matching with contracts," Games and Economic Behavior, Elsevier, vol. 102(C), pages 56-68.
    3. Klijn Flip, 2019. "Constrained Allocation of Projects to Heterogeneous Workers with Preferences over Peers," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 19(1), pages 1-9, January.
    4. Goto, Masahiro & Iwasaki, Atsushi & Kawasaki, Yujiro & Yasuda, Yosuke & Yokoo, Makoto, 2014. "Improving Fairness and Efficiency in Matching with Distributional Constraints: An Alternative Solution for the Japanese Medical Residency Match," MPRA Paper 53409, University Library of Munich, Germany.
    5. Kolos Csaba Ágoston & Péter Biró & Iain McBride, 2016. "Integer programming methods for special college admissions problems," Journal of Combinatorial Optimization, Springer, vol. 32(4), pages 1371-1399, November.
    6. Cechlárová, Katarína & Fleiner, Tamás, 2017. "Pareto optimal matchings with lower quotas," Mathematical Social Sciences, Elsevier, vol. 88(C), pages 3-10.
    7. Committee, Nobel Prize, 2020. "Improvements to auction theory and inventions of new auction formats," Nobel Prize in Economics documents 2020-2, Nobel Prize Committee.
    8. Marek Bojko, 2020. "The Probabilistic Serial and Random Priority Mechanisms with Minimum Quotas," Papers 2012.11028, arXiv.org.
    9. Kojima, Fuhito & Tamura, Akihisa & Yokoo, Makoto, 2018. "Designing matching mechanisms under constraints: An approach from discrete convex analysis," Journal of Economic Theory, Elsevier, vol. 176(C), pages 803-833.
    10. Haris Aziz & Serge Gaspers & Zhaohong Sun & Toby Walsh, 2020. "From Matching with Diversity Constraints to Matching with Regional Quotas," Papers 2002.06748, arXiv.org.
    11. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, New Economic School (NES).
    12. Sebastian Montano Correa, 2015. "Compulsory Social Service Matching Market for Physicians in Colombia," Documentos CEDE 12856, Universidad de los Andes, Facultad de Economía, CEDE.
    13. Ju, Yan & Lin, Deguang & Wang, Dazhong, 2018. "Affirmative action in school choice: A new solution," Mathematical Social Sciences, Elsevier, vol. 92(C), pages 1-9.
    14. Kolos Csaba Agoston & Peter Biro & Iain McBride, 2016. "Integer programming methods for special college admissions problems," CERS-IE WORKING PAPERS 1632, Institute of Economics, Centre for Economic and Regional Studies.
    15. Katarina Cechlarova & Bettina Klaus & David F.Manlove, 2018. "Pareto optimal matchings of students to courses in the presence of prerequisites," Cahiers de Recherches Economiques du Département d'économie 16.04, Université de Lausanne, Faculté des HEC, Département d’économie.
    16. William Thomson, 2018. "On the terminology of economic design: a critical assessment and some proposals," Review of Economic Design, Springer;Society for Economic Design, vol. 22(1), pages 67-99, June.
    17. Andreas Darmann & Janosch Döcker & Britta Dorn & Sebastian Schneckenburger, 2022. "Simplified group activity selection with group size constraints," International Journal of Game Theory, Springer;Game Theory Society, vol. 51(1), pages 169-212, March.
    18. Muhammad Maaz & Anastasios Papanastasiou, 2020. "Matching with Compatibility Constraints: The Case of the Canadian Medical Residency Match," Department of Economics Working Papers 2020-15, McMaster University.
    19. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, Center for Economic and Financial Research (CEFIR).
    20. Madhav Raghavan, 2017. "Serial Priority in Project Allocation: A Characterisation," Cahiers de Recherches Economiques du Département d'économie 17.17, Université de Lausanne, Faculté des HEC, Département d’économie.

    More about this item

    Keywords

    maximum matching; many-to-one matching; project allocation; inapproximability; bounded treewidth;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:has:discpr:1724. 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: Nora Horvath (email available below). General contact details of provider: https://edirc.repec.org/data/iehashu.html .

    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.