The Greedy Algorithm Is not Optimal for On-Line Edge Coloring

Authors Amin Saberi, David Wajc



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Author Details

Amin Saberi
  • Stanford University, CA, USA
David Wajc
  • Stanford University, CA, USA

Acknowledgements

We thank Janardhan Kulkarni for drawing our attention to [Karloff and Shmoys, 1987].

Cite As Get BibTex

Amin Saberi and David Wajc. The Greedy Algorithm Is not Optimal for On-Line Edge Coloring. In 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 198, pp. 109:1-109:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.ICALP.2021.109

Abstract

Nearly three decades ago, Bar-Noy, Motwani and Naor showed that no online edge-coloring algorithm can edge color a graph optimally. Indeed, their work, titled "the greedy algorithm is optimal for on-line edge coloring", shows that the competitive ratio of 2 of the naïve greedy algorithm is best possible online. However, their lower bound required bounded-degree graphs, of maximum degree Δ = O(log n), which prompted them to conjecture that better bounds are possible for higher-degree graphs. While progress has been made towards resolving this conjecture for restricted inputs and arrivals or for random arrival orders, an answer for fully general adversarial arrivals remained elusive.
We resolve this thirty-year-old conjecture in the affirmative, presenting a (1.9+o(1))-competitive online edge coloring algorithm for general graphs of degree Δ = ω(log n) under vertex arrivals. At the core of our results, and of possible independent interest, is a new online algorithm which rounds a fractional bipartite matching x online under vertex arrivals, guaranteeing that each edge e is matched with probability (1/2+c)⋅ x_e, for a constant c > 0.027.

Subject Classification

ACM Subject Classification
  • Theory of computation → Online algorithms
Keywords
  • Online algorithms
  • edge coloring
  • greedy
  • online matching

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References

  1. Gagan Aggarwal, Rajeev Motwani, Devavrat Shah, and An Zhu. Switch scheduling via randomized edge coloring. In Proceedings of the 44th Symposium on Foundations of Computer Science (FOCS), pages 502-512, 2003. Google Scholar
  2. Itai Ashlagi, Maximilien Burq, Chinmoy Dutta, Patrick Jaillet, Amin Saberi, and Chris Sholley. Edge weighted online windowed matching. In Proceedings of the 20th ACM Conference on Economics and Computation (EC), pages 729-742, 2019. Google Scholar
  3. Bahman Bahmani, Aranyak Mehta, and Rajeev Motwani. Online graph edge-coloring in the random-order arrival model. Theory of Computing, 8(1):567-595, 2012. Google Scholar
  4. Amotz Bar-Noy, Rajeev Motwani, and Joseph Naor. The greedy algorithm is optimal for on-line edge coloring. Information Processing Letters (IPL), 44(5):251-253, 1992. Google Scholar
  5. Sayan Bhattacharya, Fabrizio Grandoni, and David Wajc. Online edge coloring algorithms via the nibble method. In Proceedings of the 32nd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 2830-2842, 2021. Google Scholar
  6. Niv Buchbinder, Kamal Jain, and Joseph (Seffi) Naor. Online primal-dual algorithms for maximizing ad-auctions revenue. In Proceedings of the 15th Annual European Symposium on Algorithms (ESA), pages 253-264. Springer, 2007. Google Scholar
  7. Yi-Jun Chang, Qizheng He, Wenzheng Li, Seth Pettie, and Jara Uitto. The complexity of distributed edge coloring with small palettes. In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 2633-2652, 2018. Google Scholar
  8. Moses Charikar and Paul Liu. Improved algorithms for edge colouring in the w-streaming model. In Proceedings of the 4th Symposium on Simplicity in Algorithms (SOSA), pages 181-183, 2021. Google Scholar
  9. Ilan Reuven Cohen, Binghui Peng, and David Wajc. Tight bounds for online edge coloring. In Proceedings of the 60th Symposium on Foundations of Computer Science (FOCS), pages 1-25, 2019. Google Scholar
  10. Ilan Reuven Cohen and David Wajc. Randomized online matching in regular graphs. In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 960-979, 2018. Google Scholar
  11. Richard Cole, Kirstin Ost, and Stefan Schirra. Edge-coloring bipartite multigraphs in O(E log D) time. Combinatorica, 21(1):5-12, 2001. Google Scholar
  12. Nikhil R Devanur, Kamal Jain, and Robert D Kleinberg. Randomized primal-dual analysis of ranking for online bipartite matching. In Proceedings of the 24th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 101-107, 2013. Google Scholar
  13. Ran Duan, Haoqing He, and Tianyi Zhang. Dynamic edge coloring with improved approximation. In Proceedings of the 30th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 1937-1945, 2019. Google Scholar
  14. Devdatt Dubhashi and Desh Ranjan. Balls and bins: A study in negative dependence. BRICS Report Series, 3(25), 1996. Google Scholar
  15. Tomer Ezra, Michal Feldman, Nick Gravin, and Zhihao Gavin Tang. Online stochastic max-weight matching: prophet inequality for vertex and edge arrival models. In Proceedings of the 21st ACM Conference on Economics and Computation (EC), pages 769-787, 2020. Google Scholar
  16. Matthew Fahrbach, Zhiyi Huang, Runzhou Tao, and Morteza Zadimoghaddam. Edge-weighted online bipartite matching. In Proceedings of the 61st Symposium on Foundations of Computer Science (FOCS), pages 412-423, 2020. Google Scholar
  17. Jon Feldman, Nitish Korula, Vahab Mirrokni, S Muthukrishnan, and Martin Pál. Online ad assignment with free disposal. In Proceedings of the 5th Conference on Web and Internet Economics (WINE), pages 374-385, 2009. Google Scholar
  18. Yiding Feng, Rad Niazadeh, and Amin Saberi. Two-stage stochastic matching with application to ride hailing. In Proceedings of the 32nd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 2862-2877, 2021. Google Scholar
  19. Buddhima Gamlath, Michael Kapralov, Andreas Maggiori, Ola Svensson, and David Wajc. Online matching with general arrivals. In Proceedings of the 60th Symposium on Foundations of Computer Science (FOCS), pages 26-37, 2019. Google Scholar
  20. Ian Holyer. The np-completeness of edge-coloring. SIAM Journal on Computing (SICOMP), 10(4):718-720, 1981. Google Scholar
  21. Zhiyi Huang, Ning Kang, Zhihao Gavin Tang, Xiaowei Wu, Yuhao Zhang, and Xue Zhu. Fully online matching. Journal of the ACM (JACM), 67(3):1-25, 2020. Google Scholar
  22. Zhiyi Huang, Zhihao Gavin Tang, Xiaowei Wu, and Yuhao Zhang. Fully online matching ii: Beating ranking and water-filling. In Proceedings of the 61st Symposium on Foundations of Computer Science (FOCS), pages 1380-1391, 2020. Google Scholar
  23. Kumar Joag-Dev and Frank Proschan. Negative association of random variables with applications. The Annals of Statistics, pages 286-295, 1983. Google Scholar
  24. Bala Kalyanasundaram and Kirk R Pruhs. An optimal deterministic algorithm for online b-matching. Theoretical Computer Science (TCS), 233(1):319-325, 2000. Google Scholar
  25. Howard J. Karloff and David B. Shmoys. Efficient parallel algorithms for edge coloring problems. J. Algorithms, 8(1):39-52, 1987. Google Scholar
  26. Richard M Karp, Umesh V Vazirani, and Vijay V Vazirani. An optimal algorithm for on-line bipartite matching. In Proceedings of the 22nd Annual ACM Symposium on Theory of Computing (STOC), pages 352-358, 1990. Google Scholar
  27. Alam Khursheed and KM Lai Saxena. Positive dependence in multivariate distributions. Communications in Statistics - Theory and Methods, 10(12):1183-1196, 1981. Google Scholar
  28. Dénes König. Über graphen und ihre anwendung auf determinantentheorie und mengenlehre. Mathematische Annalen, 77(4):453-465, 1916. Google Scholar
  29. Aranyak Mehta. Online matching and ad allocation. Foundations and Trendsregistered in Theoretical Computer Science, 8(4):265-368, 2013. Google Scholar
  30. Aranyak Mehta, Amin Saberi, Umesh Vazirani, and Vijay Vazirani. Adwords and generalized online matching. Journal of the ACM (JACM), 54(5):22, 2007. Google Scholar
  31. Rajeev Motwani, Joseph (Seffi) Naor, and Moni Naor. The probabilistic method yields deterministic parallel algorithms. Journal of Computer and System Sciences, 49(3):478-516, 1994. Google Scholar
  32. Joseph Seffi Naor and David Wajc. Near-optimum online ad allocation for targeted advertising. ACM Transactions on Economics and Computation (TEAC), 6(3-4):16:1-16:20, 2018. Google Scholar
  33. Chrisots Papadimitriou, Tristan Pollner, Amin Saberi, and David Wajc. Online stochastic max-weight bipartite matching: Beyond prophet inequalities. In Proceedings of the 22nd ACM Conference on Economics and Computation (EC), 2021. To appear. Google Scholar
  34. Hsin-Hao Su and Hoa T. Vu. Towards the locality of vizing’s theorem. In Proceedings of the 51st Annual ACM Symposium on Theory of Computing (STOC), pages 355-364, 2019. Google Scholar
  35. Vadim G Vizing. On an estimate of the chromatic class of a p-graph. Diskret analiz, 3:25-30, 1964. Google Scholar
  36. David Wajc. Rounding dynamic matchings against an adaptive adversary. In Proceedings of the 52nd Annual ACM Symposium on Theory of Computing (STOC), pages 194-207, 2020. Google Scholar
  37. Yajun Wang and Sam Chiu-wai Wong. Two-sided online bipartite matching and vertex cover: Beating the greedy algorithm. In Proceedings of the 42nd International Colloquium on Automata, Languages and Programming (ICALP), pages 1070-1081, 2015. Google Scholar
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