Abstract
It has been shown that Lattice Linear Predicate (LLP) algorithm solves many combinatorial optimization problems such as the shortest path problem, the stable marriage problem and the market clearing price problem. In this paper, we give an LLP algorithm for the Housing Market problem. The Housing Market problem is a one-sided matching problem with n agents and n houses. Each agent has an initial allocation of a house and a totally ordered preference list of houses. The goal is to find a matching between agents and houses such that no strict subset of agents can improve their outcome by exchanging houses with each other rather than going with the matching. Gale’s celebrated Top Trading Cycle algorithm to find the matching requires \(O(n^2)\) time. Our parallel algorithm has expected time complexity \(O(n \log ^2 n)\) with and expected work complexity of \(O(n^2 \log n)\).
Supported by the NSF Grant CCR-1812351 and Cullen Trust Endowed Professorship.
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Notes
- 1.
The class CC (Comparator Circuits) is the complexity class containing decision problems which can be solved by comparator circuits of polynomial size.
References
Shapley, L., Scarf, H.: On cores and indivisibility. J. Math. Econ. 1(1), 23–37 (1974)
Hylland, A., Zeckhauser, R.: The efficient allocation of individuals to positions. J. Polit. Econ. 87(2), 293–314 (1979)
Zhou, L.: On a conjecture by Gale about one-sided matching problems. J. Econ. Theory 52(1), 123–135 (1990)
Abdulkadiroğlu, A., Sönmez, T.: Random serial dictatorship and the core from random endowments in house allocation problems. Econometrica 66(3), 689–701 (1998)
Abdulkadiroğlu, A., Sönmez, T.: House allocation with existing tenants. J. Econ. Theory 88(2), 233–260 (1999)
Roth, A.E., Postlewaite, A.: Weak versus strong domination in a market with indivisible goods. J. Math. Econ. 4(2), 131–137 (1977)
Roth, A.E.: Incentive compatibility in a market with indivisible goods. Econ. Lett. 9(2), 127–132 (1982)
David, M.: Algorithmics of Matching Under Preferences, vol. 2. World Scientific (2013)
Garg, V.K.: Predicate detection to solve combinatorial optimization problems. In: Scheideler, C., Spear, M. (eds.) SPAA 2020: 32nd ACM Symposium on Parallelism in Algorithms and Architectures, Virtual Event, USA, 15–17 July 2020, pp. 235–245. ACM (2020)
Zheng, X., Garg, V.K.: Parallel and distributed algorithms for the housing allocation problem. In: Felber, P., Friedman, R., Gilbert, S., Miller, A. (eds.) 23rd International Conference on Principles of Distributed Systems, OPODIS 2019, Neuchâtel, Switzerland, 17–19 December 2019. LIPIcs, vol. 153, pp. 23:1–23:16. Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)
Garg, V.K.: A lattice linear predicate parallel algorithm for the dynamic programming problems. CoRR, abs/2103.06264 (2021)
Chase, C.M., Garg, V.K.: Detection of global predicates: techniques and their limitations. Distrib. Comput. 11(4), 191–201 (1998)
JáJá, J.: An Introduction to Parallel Algorithms, vol. 17. Addison-Wesley Reading, Boston (1992)
Acknowledgments
I thank Changyong Hu, Robert Streit, and Xiong Zheng for various discussions on the housing allocation problem. I also thank the anonymous reviewers for comments on the paper.
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Garg, V.K. (2021). A Lattice Linear Predicate Parallel Algorithm for the Housing Market Problem. In: Johnen, C., Schiller, E.M., Schmid, S. (eds) Stabilization, Safety, and Security of Distributed Systems. SSS 2021. Lecture Notes in Computer Science(), vol 13046. Springer, Cham. https://doi.org/10.1007/978-3-030-91081-5_8
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