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Towards the Characterization of Terminal Cut Functions: a Condition for Laminar Families (2310.11367v2)

Published 17 Oct 2023 in cs.DS, cs.DM, and math.CO

Abstract: We study the following characterization problem. Given a set $T$ of terminals and a $(2{|T|}-2)$-dimensional vector $\pi$ whose coordinates are indexed by proper subsets of $T$, is there a graph $G$ that contains $T$, such that for all subsets $\emptyset\subsetneq S\subsetneq T$, $\pi_S$ equals the value of the min-cut in $G$ separating $S$ from $T\setminus S$? The only known necessary conditions are submodularity and a special class of linear inequalities given by Chaudhuri, Subrahmanyam, Wagner and Zaroliagis. Our main result is a new class of linear inequalities concerning laminar families, that generalize all previous ones. Using our new class of inequalities, we can generalize Karger's approximate min-cut counting result to graphs with terminals.

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References (28)
  1. Towards (1+ε𝜀\varepsilonitalic_ε)-approximate flow sparsifiers. In Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms, pages 279–293. SIAM, 2014.
  2. Approximate minimum cuts and their enumeration. In Symposium on Simplicity in Algorithms (SOSA), pages 36–41. SIAM, 2023.
  3. András A Benczúr. A representation of cuts within 6/5 times the edge connectivity with applications. In Proceedings of IEEE 36th Annual Foundations of Computer Science, pages 92–102. IEEE, 1995.
  4. Fast dynamic cuts, distances and effective resistances via vertex sparsifiers. In 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS), pages 1135–1146. IEEE, 2020.
  5. Julia Chuzhoy. On vertex sparsifiers with steiner nodes. In Proceedings of the forty-fourth annual ACM symposium on Theory of computing, pages 673–688, 2012.
  6. Almost-linear ε𝜀\varepsilonitalic_ε-emulators for planar graphs. In Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing, pages 1311–1324, 2022.
  7. Vertex sparsifiers and abstract rounding algorithms. In Foundations of Computer Science (FOCS), 2010 51st Annual IEEE Symposium on, pages 265–274. IEEE, 2010.
  8. Computing mimicking networks. Algorithmica, 26(1):31–49, 2000.
  9. Yu Chen and Zihan Tan. On (1+eps) -approximate flow sparsifiers. arXiv preprint arXiv:2310.07857, 2023.
  10. Realization of set functions as cut functions of graphs and hypergraphs. Discrete Mathematics, 226(1-3):199–210, 2001.
  11. Improved guarantees for vertex sparsification in planar graphs. arXiv preprint arXiv:1702.01136, 2017.
  12. The expander hierarchy and its applications to dynamic graph algorithms. In Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 2212–2228. SIAM, 2021.
  13. Characterizing multiterminal flow networks and computing flows in networks of small treewidth. Journal of Computer and System Sciences, 57(3):366–375, 1998.
  14. On the number of small cuts in a graph. Information Processing Letters, 59(1):41–44, 1996.
  15. Kamal Jain. A factor 2 approximation algorithm for the generalized steiner network problem. Combinatorica, 21:39–60, 2001.
  16. David R Karger. Global min-cuts in rnc, and other ramifications of a simple min-cut algorithm. In Soda, volume 93, pages 21–30. Citeseer, 1993.
  17. David R Karger. Minimum cuts in near-linear time. Journal of the ACM (JACM), 47(1):46–76, 2000.
  18. Exact flow sparsification requires unbounded size. In Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 2354–2367. SIAM, 2023.
  19. An exponential lower bound for cut sparsifiers in planar graphs. arXiv preprint arXiv:1706.06086, 2017.
  20. Mimicking networks and succinct representations of terminal cuts. In Proceedings of the twenty-fourth annual ACM-SIAM symposium on Discrete algorithms, pages 1789–1799. SIAM, 2013.
  21. On mimicking networks representing minimum terminal cuts. Information Processing Letters, 114(7):365–371, 2014.
  22. Refined vertex sparsifiers of planar graphs. arXiv preprint arXiv:1702.05951, 2017.
  23. Representative sets and irrelevant vertices: New tools for kernelization. In 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science, pages 450–459. IEEE, 2012.
  24. Extensions and limits to vertex sparsification. In Proceedings of the forty-second ACM symposium on Theory of computing, pages 47–56. ACM, 2010.
  25. Wolfgang Mader. A reduction method for edge-connectivity in graphs. In Annals of Discrete Mathematics, volume 3, pages 145–164. Elsevier, 1978.
  26. Ankur Moitra. Approximation algorithms for multicommodity-type problems with guarantees independent of the graph size. In Foundations of Computer Science, 2009. FOCS’09. 50th Annual IEEE Symposium on, pages 3–12. IEEE, 2009.
  27. Computing all small cuts in an undirected network. SIAM Journal on Discrete Mathematics, 10(3):469–481, 1997.
  28. Yutaro Yamaguchi. Realizing symmetric set functions as hypergraph cut capacity. Discrete Mathematics, 339(8):2007–2017, 2016.
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