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# On Randomized Algorithms for Matching in the Online Preemptive Model

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9294))

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## Abstract

We investigate the power of randomized algorithms for the maximum cardinality matching (MCM) and the maximum weight matching (MWM) problems in the online preemptive model. In this model, the edges of a graph are revealed one by one and the algorithm is required to always maintain a valid matching. On seeing an edge, the algorithm has to either accept or reject the edge. If accepted, then the adjacent edges are discarded. The complexity of the problem is settled for deterministic algorithms [7, 9].

Almost nothing is known for randomized algorithms. A lower bound of 1.693 is known for MCM with a trivial upper bound of two. An upper bound of 5.356 is known for MWM. We initiate a systematic study of the same in this paper with an aim to isolate and understand the difficulty. We begin with a primal-dual analysis of the deterministic algorithm due to [7]. All deterministic lower bounds are on instances which are trees at every step. For this class of (unweighted) graphs we present a randomized algorithm which is $$\frac{28}{15}$$-competitive. The analysis is a considerable extension of the (simple) primal-dual analysis for the deterministic case. The key new technique is that the distribution of primal charge to dual variables depends on the “neighborhood” and needs to be done after having seen the entire input. The assignment is asymmetric: in that edges may assign different charges to the two end-points. Also the proof depends on a non-trivial structural statement on the performance of the algorithm on the input tree.

The other main result of this paper is an extension of the deterministic lower bound of Varadaraja [9] to a natural class of randomized algorithms which decide whether to accept a new edge or not using independent random choices. This indicates that randomized algorithms will have to use dependent coin tosses to succeed. Indeed, the few known randomized algorithms, even in very restricted models follow this.

We also present the best possible $$\frac{4}{3}$$-competitive randomized algorithm for MCM on paths.

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## References

1. Buchbinder, N., Naor, J.: The Design of Competitive Online Algorithms via a Primal-Dual Approach. Foundations and Trends in Theoretical Computer Science 3(2-3), 93–263 (2009)

2. Chan, T.H., Chen, F., Wu, X., Zhao, Z.: Ranking on Arbitrary Graphs: Rematch via Continuous LP with Monotone and Boundary Condition Constraints. In: Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2014, Portland, Oregon, USA, January 5-7, pp. 1112–1122 (2014)

3. Chiplunkar, A., Tirodkar, S., Vishwanathan, S.: On Randomized Algorithms for Matching in the Online Preemptive Model. CoRR abs/1412.8615 (2014). http://arxiv.org/abs/1412.8615

4. Epstein, L., Levin, A., Segev, D., Weimann, O.: Improved Bounds for Online Preemptive Matching. In: 30th International Symposium on Theoretical Aspects of Computer Science, STACS 2013, Kiel, Germany, pp. 389–399 (2013)

5. Feigenbaum, J., Kannan, S., McGregor, A., Suri, S., Zhang, J.: On Graph Problems in a Semi-streaming Model. Theor. Comput. Sci. 348(2), 207–216 (2005)

6. Karp, R.M., Vazirani, U.V., Vazirani, V.V.: An Optimal Algorithm for On-line Bipartite Matching. In: Proceedings of the Twenty-Second Annual ACM Symposium on Theory of Computing, STOC 1990, pp. 352–358. ACM, New York (1990)

7. McGregor, A.: Finding Graph Matchings in Data Streams. In: Chekuri, C., Jansen, K., Rolim, J.D.P., Trevisan, L. (eds.) APPROX 2005 and RANDOM 2005. LNCS, vol. 3624, pp. 170–181. Springer, Heidelberg (2005)

8. Poloczek, M., Szegedy, M.: Randomized Greedy Algorithms for the Maximum Matching Problem with New Analysis. In: 53rd Annual IEEE Symposium on Foundations of Computer Science, FOCS 2012, New Brunswick, NJ, USA, pp. 708–717 (2012)

9. Badanidiyuru Varadaraja, A.: Buyback Problem - Approximate Matroid Intersection with Cancellation Costs. In: Aceto, L., Henzinger, M., Sgall, J. (eds.) ICALP 2011, Part I. LNCS, vol. 6755, pp. 379–390. Springer, Heidelberg (2011)

10. Yao, A.C.C.: Probabilistic computations: Toward a unified measure of complexity. In: 18th Annual Symposium on Foundations of Computer Science, FOCS 1977, pp. 222–227 (October 1977)

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Correspondence to Ashish Chiplunkar .

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Chiplunkar, A., Tirodkar, S., Vishwanathan, S. (2015). On Randomized Algorithms for Matching in the Online Preemptive Model. In: Bansal, N., Finocchi, I. (eds) Algorithms - ESA 2015. Lecture Notes in Computer Science(), vol 9294. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48350-3_28

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• DOI: https://doi.org/10.1007/978-3-662-48350-3_28

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• Publisher Name: Springer, Berlin, Heidelberg

• Print ISBN: 978-3-662-48349-7

• Online ISBN: 978-3-662-48350-3

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