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Scaling Algorithms for Weighted Matching in General Graphs

Published:03 January 2018Publication History
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Abstract

We present a new scaling algorithm for maximum (or minimum) weight perfect matching on general, edge weighted graphs. Our algorithm runs in O(mnlog(nN)) time, O(mn) per scale, which matches the running time of the best cardinality matching algorithms on sparse graphs [16, 20, 36, 37]. Here, m,n, and N bound the number of edges, vertices, and magnitude, respectively, of any integer edge weight. Our result improves on a 25-year-old algorithm of Gabow and Tarjan, which runs in O(mnlog nα (m,n) log(nN)) time.

References

  1. G. Birkhoff. 1946. Tres observaciones sobre el elgebra lineal. Universidad Nacional de Tucuman, Revista A 5, 1--2, 147--151.Google ScholarGoogle Scholar
  2. M. B. Cohen, A. Madry, P. Sankowski, and A. Vladu. 2017. Negative-weight shortest paths and unit capacity minimum cost flow in time. In Proceedings of the 28th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA’17). 752--771. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. W. H. Cunningham and A. B. Marsh, III. 1978. A primal algorithm for optimum matching. Math. Program. Study 8, 50--72.Google ScholarGoogle ScholarCross RefCross Ref
  4. M. Cygan, H. N. Gabow, and P. Sankowski. 2015. Algorithmic applications of Baur-Strassen’s theorem: Shortest cycles, diameter, and matchings. J. ACM 62, 4, 28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. R. Duan and S. Pettie. 2014. Linear-time approximation for maximum weight matching. J. ACM 61, 1, 1. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. R. Duan and H.-H. Su. 2012. A scaling algorithm for maximum weight matching in bipartite graphs. In Proceedings of the 23rd ACM-SIAM Symposium on Discrete Algorithms (SODA’12). 1413--1424. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. Edmonds. 1965. Maximum matching and a polyhedron with -vertices. J. Res. Nat. Bur. Stand. Sect. B 69B, 125--130.Google ScholarGoogle ScholarCross RefCross Ref
  8. J. Edmonds 1965. Paths, trees, and flowers. Can. J. Math. 17, 449--467.Google ScholarGoogle ScholarCross RefCross Ref
  9. J. Edmonds and R. M. Karp 1972. Theoretical improvements in algorithmic efficiency for network flow problems. J. ACM 19, 2, 248--264. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. M. L. Fredman and R. E. Tarjan. 1987. Fibonacci heaps and their uses in improved network optimization algorithms. J. ACM 34, 3, 596--615. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. L. Fredman and D. E. Willard. 1994. Trans-dichotomous algorithms for minimum spanning trees and shortest paths. J. Comput. Syst. Sci. 48, 3, 533--551. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. H. N. Gabow. 1976. An efficient implementation of Edmonds’ algorithm for maximum matching on graphs. J. ACM 23, 221--234. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. H. N. Gabow. 1985. A scaling algorithm for weighted matching on general graphs. In Proceedings of the 26th Annual Symposium on Foundations of Computer Science (FOCS’85). 90--100. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. H. N. Gabow. 1990. Data structures for weighted matching and nearest common ancestors with linking. In Proceedings of the 1st Annual ACM-SIAM Symposium on Discrete Algorithms (SODA’90). 434--443. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. H. N. Gabow. 2016. Data structures for weighted matching and extensions to -matching and -factors. CoRR, abs/1611.07541.Google ScholarGoogle Scholar
  16. H. N. Gabow. 2017. The weighted matching approach to maximum cardinality matching. Fundam. Inform. 154, 1--4, 109--130.Google ScholarGoogle ScholarCross RefCross Ref
  17. H. N. Gabow, Z. Galil, and T. H. Spencer. 1989. Efficient implementation of graph algorithms using contraction. J. ACM 36, 3, 540--572. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. H. N. Gabow and R. E. Tarjan. 1985. A linear-time algorithm for a special case of disjoint set union. J. Comput. Syst. Sci. 30, 2, 209--221.Google ScholarGoogle ScholarCross RefCross Ref
  19. H. N. Gabow and R. E. Tarjan. 1989. Faster scaling algorithms for network problems. SIAM J. Comput. 18, 5, 1013--1036. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. H. N. Gabow and R. E. Tarjan. 1991. Faster scaling algorithms for general graph-matching problems. J. ACM 38, 4, 815--853. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Z. Galil, S. Micali, and H. N. Gabow. 1986. An algorithm for finding a maximal weighted matching in general graphs. SIAM J. Comput. 15, 1, 120--130. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. A. V. Goldberg and R. Kennedy. 1997. Global price updates help. SIAM J. Discr. Math. 10, 4, 551--572. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Y. Han. 2002. Deterministic sorting in time and linear space. In Proceedings of the 34th ACM Symposium on Theory of Computers (STOC’02). ACM Press, 602--608. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Y. Han and M. Thorup. 2002. Integer sorting in O(n√log log n) expected time and linear space. In Proceedings of the 43rd Symposium on Foundations of Computer Science (FOCS’02). 135--144. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. C.-C. Huang and T. Kavitha. 2012. Efficient algorithms for maximum weight matchings in general graphs with small edge weights. In Proceedings of the 23rd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA’12). 1400--1412. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. M.-Y. Kao, T. W. Lam, W.-K. Sung, and H.-F. Ting. 2001. A decomposition theorem for maximum weight bipartite matchings. SIAM J. Comput. 31, 1, 18--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. A. V. Karzanov. 1976. Efficient implementations of Edmonds’ algorithms for finding matchings with maximum cardinality and maximum weight. In Studies in Discrete Optimization, A. A. Fridman (Eds.). Nauka, Moscow, 306--327.Google ScholarGoogle Scholar
  28. E. Lawler. 1976. Combinatorial Optimization: Networks and Matroids. Holt, Rinehart 8 Winston, New York.Google ScholarGoogle Scholar
  29. S. Micali and V. Vazirani. 1980. An O(√|V| ċ |E|) algorithm for finding maximum matching in general graphs. In Proceedings of the 21st IEEE Symposium on Foundations of Computer Science (FOCS’80). 17--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. J. B. Orlin and R. K. Ahuja. 1992. New scaling algorithms for the assignment and minimum mean cycle problems. Math. Program. 54, 41--56.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. S. Pettie. 2012. A simple reduction from maximum weight matching to maximum cardinality matching. Inf. Process. Lett. 112, 23, 893--898. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. S. Pettie. 2015. Sensitivity analysis of minimum spanning trees in sub-inverse-Ackermann time. J. Graph Algor. Appl. 19, 1, 375--391.Google ScholarGoogle ScholarCross RefCross Ref
  33. M. Thorup. 1999. Undirected single-source shortest paths with positive integer weights in linear time. J. ACM 46, 3, 362--394. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. M. Thorup. 2003. Integer priority queues with decrease key in constant time and the single source shortest paths problem. In Proceedings of the 35th ACM Symposium on Theory of Computing (STOC’03). 149--158. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. M. Thorup. 2007. Equivalence between priority queues and sorting. J. ACM 54, 6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. V. V. Vazirani. 2012. An improved definition of blossoms and a simpler proof of the MV matching algorithm. CoRR, abs/1210.4594.Google ScholarGoogle Scholar
  37. V. V. Vazirani. 2014. A proof of the MV matching algorithm. Unpublished manuscript.Google ScholarGoogle Scholar
  38. J. von Neumann. 1953. A certain zero-sum two-person game equivalent to the optimal assignment problem. In Contributions to the Theory of Games, vol. II, H. W. Kuhn and A. W. Tucker (Eds.). Princeton University Press, 5--12.Google ScholarGoogle Scholar

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          cover image ACM Transactions on Algorithms
          ACM Transactions on Algorithms  Volume 14, Issue 1
          January 2018
          269 pages
          ISSN:1549-6325
          EISSN:1549-6333
          DOI:10.1145/3171590
          Issue’s Table of Contents

          Copyright © 2018 ACM

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          Publication History

          • Published: 3 January 2018
          • Revised: 1 October 2017
          • Accepted: 1 October 2017
          • Received: 1 February 2017
          Published in talg Volume 14, Issue 1

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