IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v44y2022i2d10.1007_s10878-022-00889-4.html
   My bibliography  Save this article

Computing the sequence of k-cardinality assignments

Author

Listed:
  • Amnon Rosenmann

    (Graz University of Technology)

Abstract

The k-cardinality assignment (k-assignment, for short) problem asks for finding a minimal (maximal) weight of a matching of cardinality k in a weighted bipartite graph $$K_{n,n}$$ K n , n , $$k \le n$$ k ≤ n . Here we are interested in computing the sequence of all k-assignments, $$k=1,\ldots ,n$$ k = 1 , … , n . By applying the algorithm of Gassner and Klinz (2010) for the parametric assignment problem one can compute in time $${\mathcal {O}}(n^3)$$ O ( n 3 ) the set of k-assignments for those integers $$k \le n$$ k ≤ n which refer to essential terms of the full characteristic maxpolynomial $${\bar{\chi }}_{W}(x)$$ χ ¯ W ( x ) of the corresponding max-plus weight matrix W. We show that $${\bar{\chi }}_{W}(x)$$ χ ¯ W ( x ) is in full canonical form, which implies that the remaining k-assignments refer to semi-essential terms of $${\bar{\chi }}_{W}(x)$$ χ ¯ W ( x ) . This property enables us to efficiently compute in time $${\mathcal {O}}(n^2)$$ O ( n 2 ) all the remaining k-assignments out of the already computed essential k-assignments. It follows that time complexity for computing the sequence of all k-cardinality assignments is $${\mathcal {O}}(n^3)$$ O ( n 3 ) , which is the best known time for this problem.

Suggested Citation

  • Amnon Rosenmann, 2022. "Computing the sequence of k-cardinality assignments," Journal of Combinatorial Optimization, Springer, vol. 44(2), pages 1265-1283, September.
  • Handle: RePEc:spr:jcomop:v:44:y:2022:i:2:d:10.1007_s10878-022-00889-4
    DOI: 10.1007/s10878-022-00889-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-022-00889-4
    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/s10878-022-00889-4?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. Volgenant, A., 2004. "Solving the k-cardinality assignment problem by transformation," European Journal of Operational Research, Elsevier, vol. 157(2), pages 322-331, September.
    2. H. W. Kuhn, 1956. "Variants of the hungarian method for assignment problems," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 3(4), pages 253-258, December.
    3. Asoke Kumar Bhunia & Amiya Biswas & Subhra Sankha Samanta, 2017. "A genetic algorithm-based approach for unbalanced assignment problem in interval environment," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 27(1), pages 62-77.
    4. H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
    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. Xiaojuan Ning & Yule Liu & Yishu Ma & Zhiwei Lu & Haiyan Jin & Zhenghao Shi & Yinghui Wang, 2024. "TSPconv-Net: Transformer and Sparse Convolution for 3D Instance Segmentation in Point Clouds," Mathematics, MDPI, vol. 12(18), pages 1-15, September.
    2. Ekta Jain & Kalpana Dahiya & Vanita Verma, 2020. "A priority based unbalanced time minimization assignment problem," OPSEARCH, Springer;Operational Research Society of India, vol. 57(1), pages 13-45, March.
    3. Helena Gaspars-Wieloch, 2021. "The Assignment Problem in Human Resource Project Management under Uncertainty," Risks, MDPI, vol. 9(1), pages 1-17, January.
    4. Ivan Belik & Kurt Jornsten, 2018. "Critical objective function values in linear sum assignment problems," Journal of Combinatorial Optimization, Springer, vol. 35(3), pages 842-852, April.
    5. Weiqiang Shen & Chuanlin Zhang & Xiaona Zhang & Jinglun Shi, 2019. "A fully distributed deployment algorithm for underwater strong k-barrier coverage using mobile sensors," International Journal of Distributed Sensor Networks, , vol. 15(4), pages 15501477198, April.
    6. Bo Cowgill & Jonathan M. V. Davis & B. Pablo Montagnes & Patryk Perkowski, 2024. "Stable Matching on the Job? Theory and Evidence on Internal Talent Markets," CESifo Working Paper Series 11120, CESifo.
    7. András Frank, 2005. "On Kuhn's Hungarian Method—A tribute from Hungary," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(1), pages 2-5, February.
    8. Weihua Yang & Xu Zhang & Xia Wang, 2024. "The Wasserstein Metric between a Discrete Probability Measure and a Continuous One," Mathematics, MDPI, vol. 12(15), pages 1-13, July.
    9. Amit Kumar & Anila Gupta, 2013. "Mehar’s methods for fuzzy assignment problems with restrictions," Fuzzy Information and Engineering, Springer, vol. 5(1), pages 27-44, March.
    10. Nisse, Nicolas & Salch, Alexandre & Weber, Valentin, 2023. "Recovery of disrupted airline operations using k-maximum matching in graphs," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1061-1072.
    11. Parvin Ahmadi & Iman Gholampour & Mahmoud Tabandeh, 2018. "Cluster-based sparse topical coding for topic mining and document clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 537-558, September.
    12. Bachtenkirch, David & Bock, Stefan, 2022. "Finding efficient make-to-order production and batch delivery schedules," European Journal of Operational Research, Elsevier, vol. 297(1), pages 133-152.
    13. Omar Zatarain & Jesse Yoe Rumbo-Morales & Silvia Ramos-Cabral & Gerardo Ortíz-Torres & Felipe d. J. Sorcia-Vázquez & Iván Guillén-Escamilla & Juan Carlos Mixteco-Sánchez, 2023. "A Method for Perception and Assessment of Semantic Textual Similarities in English," Mathematics, MDPI, vol. 11(12), pages 1-20, June.
    14. Chenchen Ma & Jing Ouyang & Gongjun Xu, 2023. "Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 175-207, March.
    15. Winker, Peter, 2023. "Visualizing Topic Uncertainty in Topic Modelling," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277584, Verein für Socialpolitik / German Economic Association.
    16. Robert M. Curry & Joseph Foraker & Gary Lazzaro & David M. Ruth, 2024. "Practice Summary: Optimal Student Group Reassignment at U.S. Naval Academy," Interfaces, INFORMS, vol. 54(3), pages 205-210, May.
    17. Aidin Rezaeian & Hamidreza Koosha & Mohammad Ranjbar & Saeed Poormoaied, 2024. "The assignment of project managers to projects in an uncertain dynamic environment," Annals of Operations Research, Springer, vol. 341(2), pages 1107-1134, October.
    18. Tran Hoang Hai, 2020. "Estimation of volatility causality in structural autoregressions with heteroskedasticity using independent component analysis," Statistical Papers, Springer, vol. 61(1), pages 1-16, February.
    19. Delafield, Gemma & Smith, Greg S. & Day, Brett & Holland, Robert A. & Donnison, Caspar & Hastings, Astley & Taylor, Gail & Owen, Nathan & Lovett, Andrew, 2024. "Spatial context matters: Assessing how future renewable energy pathways will impact nature and society," Renewable Energy, Elsevier, vol. 220(C).
    20. Mehran Farzadmehr & Valentin Carlan & Thierry Vanelslander, 2023. "Contemporary challenges and AI solutions in port operations: applying Gale–Shapley algorithm to find best matches," Journal of Shipping and Trade, Springer, vol. 8(1), pages 1-44, December.

    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:44:y:2022:i:2:d:10.1007_s10878-022-00889-4. 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.