Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Toward a Better Understanding of Randomized Greedy Matching

Published: 30 November 2023 Publication History
  • Get Citation Alerts
  • Abstract

    There has been a long history of studying randomized greedy matching algorithms since the work by Dyer and Frieze [9]. We follow this trend and consider the problem formulated in the oblivious setting, in which the vertex set of a graph is known to the algorithm but not the edge set. The algorithm can make queries for the existence of the edge between any pair of vertices but must include the edge into the matching if it exists, i.e., as in the query-commit model by Gamlath et al. [12]. We revisit the Modified Randomized Greedy (MRG) algorithm by Aronson et al. [1] that is proved to achieve a (0.5+ε)-approximation. In each step of the algorithm, an unmatched vertex is chosen uniformly at random and matched to a randomly chosen neighbor (if exists). We study a weaker version of the algorithm named Random Decision Order (RDO) that, in each step, randomly picks an unmatched vertex and matches it to an arbitrary neighbor (if exists). We prove that the RDO algorithm provides a 0.639-approximation for bipartite graphs and 0.531-approximation for general graphs. As a corollary, we substantially improve the approximation ratio of MRG.
    Furthermore, we generalize the RDO algorithm to the edge-weighted case and prove that it achieves a 0.501-approximation ratio. This result solves the open question by Chan et al. [4] and Gamlath et al. [12] about the existence of an algorithm that beats greedy in edge-weighted general graphs, where the greedy algorithm probes the edges in descending order of edge-weights. We also present a variant of the algorithm that achieves a (1-1/e)-approximation for edge-weighted bipartite graphs, which generalizes the (1-1/e)-approximation ratio of Gamlath et al. [12] for the stochastic setting to the case when the realizations of edges are arbitrarily correlated, where in the stochastic setting, there is a known probability associated with each pair of vertices that indicates the probability that an edge exists between the two vertices, when the pair is probed.

    References

    [1]
    Jonathan Aronson, Martin Dyer, Alan Frieze, and Stephen Suen. 1995. Randomized greedy matching. II. Rand. Struct. Algor. 6, 1 (Jan.1995), 55–73.
    [2]
    Sepehr Assadi, Sanjeev Khanna, and Yang Li. 2017. The stochastic matching problem: Beating half with a non-adaptive algorithm. In EC. ACM, 99–116.
    [3]
    T.-H. Hubert Chan, Fei Chen, and Xiaowei Wu. 2018. Analyzing node-weighted oblivious matching problem via continuous LP with jump discontinuity. ACM Trans. Algor. 14, 2 (2018), 12:1–12:25.
    [4]
    T.-H. Hubert Chan, Fei Chen, Xiaowei Wu, and Zhichao Zhao. 2018. Ranking on arbitrary graphs: Rematch via continuous linear programming. SIAM J. Comput. 47, 4 (2018), 1529–1546.
    [5]
    Ning Chen, Nicole Immorlica, Anna R. Karlin, Mohammad Mahdian, and Atri Rudra. 2009. Approximating matches made in heaven. In ICALP, Lecture Notes in Computer Science, Vol. 5555. Springer, 266–278.
    [6]
    Kevin P. Costello, Prasad Tetali, and Pushkar Tripathi. 2012. Stochastic matching with commitment. In ICALP, Lecture Notes in Computer Science, Vol. 7391. Springer, 822–833.
    [7]
    Mahsa Derakhshan and Alireza Farhadi. 2023. Beating (1 - 1/e)-Approximation for weighted stochastic matching. In SODA. SIAM, 1931–1961.
    [8]
    Nikhil R. Devanur, Kamal Jain, and Robert D. Kleinberg. 2013. Randomized primal-dual analysis of RANKING for online BiPartite matching. In SODA. SIAM, 101–107.
    [9]
    Martin E. Dyer and Alan M. Frieze. 1991. Randomized greedy matching. Random Struct. Algorithms 2, 1 (1991), 29–46.
    [10]
    Matthew Fahrbach, Zhiyi Huang, Runzhou Tao, and Morteza Zadimoghaddam. 2020. Edge-weighted online bipartite matching. In FOCS. IEEE, 412–423.
    [11]
    Hu Fu, Zhihao Gavin Tang, Hongxun Wu, Jinzhao Wu, and Qianfan Zhang. 2021. Random order vertex arrival contention resolution schemes for matching, with applications. In ICALP,LIPIcs, Vol. 198. Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 68:1–68:20.
    [12]
    Buddhima Gamlath, Sagar Kale, and Ola Svensson. 2019. Beating greedy for stochastic bipartite matching. In SODA. SIAM, 2841–2854.
    [13]
    G. Goel and P. Tripathi. 2012. Matching with our eyes closed. In 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science. 718–727.
    [14]
    Zhiyi Huang. 2019. Understanding zadimoghaddam’s edge-weighted online matching algorithm: Weighted case. CoRR abs/1910.03287 (2019).
    [15]
    Zhiyi Huang, Ning Kang, Zhihao Gavin Tang, Xiaowei Wu, Yuhao Zhang, and Xue Zhu. 2018. How to match when all vertices arrive online. In STOC. ACM, 17–29.
    [16]
    Zhiyi Huang, Ning Kang, Zhihao Gavin Tang, Xiaowei Wu, Yuhao Zhang, and Xue Zhu. 2020. Fully online matching. J. ACM 67, 3 (2020), 17:1–17:25.
    [17]
    Zhiyi Huang, Binghui Peng, Zhihao Gavin Tang, Runzhou Tao, Xiaowei Wu, and Yuhao Zhang. 2019. Tight competitive ratios of classic matching algorithms in the fully online model. In SODA. SIAM, 2875–2886.
    [18]
    Zhiyi Huang, Zhihao Gavin Tang, Xiaowei Wu, and Yuhao Zhang. 2018. Online vertex-weighted bipartite matching: Beating 1-1/e with random arrivals. In ICALP,LIPIcs, Vol. 107. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 79:1–79:14.
    [19]
    Chinmay Karande, Aranyak Mehta, and Pushkar Tripathi. 2011. Online bipartite matching with unknown distributions. In STOC. ACM, 587–596.
    [20]
    Richard M. Karp, Umesh V. Vazirani, and Vijay V. Vazirani. 1990. An optimal algorithm for on-line bipartite matching. In STOC. ACM, 352–358.
    [21]
    Jacob Magun. 1998. Greedy matching algorithms: An experimental study. ACM J. Exp. Algorithmics 3 (1998), 6.
    [22]
    Mohammad Mahdian and Qiqi Yan. 2011. Online bipartite matching with random arrivals: An approach based on strongly factor-revealing LPs. In STOC. ACM, 597–606.
    [23]
    Matthias Poloczek and Mario Szegedy. 2012. Randomized greedy algorithms for the maximum matching problem with new analysis. In FOCS. IEEE Computer Society, 708–717.
    [24]
    Alvin E. Roth, Tayfun Sönmez, and M. Utku Ünver. 2005. Pairwise kidney exchange. J. Econ. Theory 125, 2 (2005), 151–188.
    [25]
    Sahil Singla. 2018. The price of information in combinatorial optimization. In SODA. SIAM, 2523–2532.
    [26]
    Zhihao Gavin Tang, Xiaowei Wu, and Yuhao Zhang. 2020. A simple 1-1/e approximation for oblivious bipartite matching. CoRR abs/2002.06037 (2020).
    [27]
    Zhihao Gavin Tang, Xiaowei Wu, and Yuhao Zhang. 2020. Towards a better understanding of randomized greedy matching. In STOC. ACM, 1097–1110.
    [28]
    Gottfried Tinhofer. 1984. A probabilistic analysis of some greedy cardinality matching algorithms. Ann. OR 1, 3 (1984), 239–254.

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image Journal of the ACM
    Journal of the ACM  Volume 70, Issue 6
    December 2023
    314 pages
    ISSN:0004-5411
    EISSN:1557-735X
    DOI:10.1145/3633310
    • Editor:
    • Venkatesan Guruswami
    Issue’s Table of Contents

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 30 November 2023
    Online AM: 06 October 2023
    Accepted: 26 July 2023
    Revised: 20 July 2023
    Received: 07 March 2021
    Published in JACM Volume 70, Issue 6

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Oblivious Matching
    2. Randomized Greedy
    3. Approximation Algorithms

    Qualifiers

    • Research-article

    Funding Sources

    • National Natural Science Foundation of China
    • Science and Technology Development Fund (FDCT) Macau SAR
    • National Natural Science Foundation of China

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 380
      Total Downloads
    • Downloads (Last 12 months)380
    • Downloads (Last 6 weeks)36

    Other Metrics

    Citations

    View Options

    Get Access

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    Full Text

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media