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Breaking the quadratic barrier for matroid intersection

Published:15 June 2021Publication History

ABSTRACT

The matroid intersection problem is a fundamental problem that has been extensively studied for half a century. In the classic version of this problem, we are given two matroids M1 = (V, I1) and M2 = (V, I2) on a comment ground set V of n elements, and then we have to find the largest common independent set SI1I2 by making independence oracle queries of the form ”Is SI1?” or ”Is SI2?” for SV. The goal is to minimize the number of queries.

Beating the existing Õ(n2) bound, known as the quadratic barrier, is an open problem that captures the limits of techniques from two lines of work. The first one is the classic Cunningham’s algorithm [SICOMP 1986], whose Õ(n2)-query implementations were shown by CLS+ [FOCS 2019] and Nguyen [2019] (more generally, these algorithms take Õ(nr) queries where r denotes the rank which can be as big as n). The other one is the general cutting plane method of Lee, Sidford, and Wong [FOCS 2015]. The only progress towards breaking the quadratic barrier requires either approximation algorithms or a more powerful rank oracle query [CLS+ FOCS 2019]. No exact algorithm with o(n2) independence queries was known.

In this work, we break the quadratic barrier with a randomized algorithm guaranteeing Õ(n9/5) independence queries with high probability, and a deterministic algorithm guaranteeing Õ(n11/6) independence queries. Our key insight is simple and fast algorithms to solve a graph reachability problem that arose in the standard augmenting path framework [Edmonds 1968]. Combining this with previous exact and approximation algorithms leads to our results.

References

  1. Martin Aigner and Thomas A. Dowling. 1971. Matching Theory for Combinatorial Geometries. Trans. Amer. Math. Soc., 158, 1, 1971. Pages 231–245.Google ScholarGoogle Scholar
  2. Deeparnab Chakrabarty, Yin Tat Lee, Aaron Sidford, Sahil Singla, and Sam Chiu-wai Wong. 2019. Faster Matroid Intersection. In FOCS. IEEE Computer Society. Pages 1146–1168. https://doi.org/10.1109/FOCS.2019.00072 Google ScholarGoogle ScholarCross RefCross Ref
  3. Chandra Chekuri and Kent Quanrud. 2016. A Fast Approximation for Maximum Weight Matroid Intersection. In SODA. SIAM. Pages 445–457. https://doi.org/10.1137/1.9781611974331.ch33 Google ScholarGoogle ScholarCross RefCross Ref
  4. William H. Cunningham. 1986. Improved Bounds for Matroid Partition and Intersection Algorithms. SIAM J. Comput., 15, 4, 1986. Pages 948–957. https://doi.org/10.1137/0215066 Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Jack Edmonds. 1970. Submodular functions, matroids, and certain polyhedra. In Combinatorial structures and their applications. Pages 69–87.Google ScholarGoogle Scholar
  6. Jack Edmonds. 1979. Matroid intersection. In Annals of discrete Mathematics. 4, Elsevier. Pages 39–49.Google ScholarGoogle Scholar
  7. Jack Edmonds, GB Dantzig, AF Veinott, and M Jünger. 1968. Matroid partition. 50 Years of Integer Programming 1958–2008, 1968. Pages 199.Google ScholarGoogle Scholar
  8. Andrei Graur, Tristan Pollner, Vidhya Ramaswamy, and S. Matthew Weinberg. 2020. New Query Lower Bounds for Submodular Function Minimization. In ITCS. LIPIcs. 151, Schloss Dagstuhl - Leibniz-Zentrum für Informatik. Pages 64:1–64:16. https://doi.org/10.4230/LIPIcs.ITCS.2020.64 Google ScholarGoogle ScholarCross RefCross Ref
  9. Nicholas J. A. Harvey. 2008. Matroid intersection, pointer chasing, and Young's seminormal representation of S\(_\mbox n\). In SODA. SIAM. Pages 542–549.Google ScholarGoogle Scholar
  10. Eugene L. Lawler. 1975. Matroid intersection algorithms. Math. Program., 9, 1, 1975. Pages 31–56. https://doi.org/10.1007/BF01681329 Google ScholarGoogle ScholarCross RefCross Ref
  11. Troy Lee, Tongyang Li, Miklos Santha, and Shengyu Zhang. 2020. On the cut dimension of a graph. CoRR, abs/2011.05085, 2020.Google ScholarGoogle Scholar
  12. Yin Tat Lee, Aaron Sidford, and Sam Chiu-wai Wong. 2015. A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization. In FOCS. IEEE Computer Society. Pages 1049–1065. https://doi.org/10.1109/FOCS.2015.68 Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Sagnik Mukhopadhyay and Danupon Nanongkai. 2020. Weighted min-cut: sequential, cut-query, and streaming algorithms. In STOC. ACM. Pages 496–509. https://doi.org/10.1145/3357713.3384334Google ScholarGoogle Scholar
  14. Huy L. Nguyen. 2019. A note on Cunningham's algorithm for matroid intersection. CoRR, abs/1904.04129, 2019.Google ScholarGoogle Scholar
  15. Aviad Rubinstein, Tselil Schramm, and S. Matthew Weinberg. 2018. Computing Exact Minimum Cuts Without Knowing the Graph. In Proceedings of the 9th ITCS. Pages 39:1–39:16. https://doi.org/10.4230/LIPIcs.ITCS.2018.39 Google ScholarGoogle ScholarCross RefCross Ref
  16. Alexander Schrijver. 2003. Combinatorial Optimization: Polyhedra and Efficiency. Springer.Google ScholarGoogle Scholar

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          cover image ACM Conferences
          STOC 2021: Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing
          June 2021
          1797 pages
          ISBN:9781450380539
          DOI:10.1145/3406325

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          • Published: 15 June 2021

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