Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Quasipolynomial Multicut-mimicking Networks and Kernels for Multiway Cut Problems

Published: 04 March 2022 Publication History
  • Get Citation Alerts
  • Abstract

    We show the existence of an exact mimicking network of kO(log k) edges for minimum multicuts over a set of terminals in an undirected graph, where k is the total capacity of the terminals, i.e., the sum of the degrees of the terminal vertices. Furthermore, using the best available approximation algorithm for Small Set Expansion, we show that a mimicking network of kO(log3 k) edges can be computed in randomized polynomial time. As a consequence, we show quasipolynomial kernels for several problems, including Edge Multiway Cut, Group Feedback Edge Set for an arbitrary group, and Edge Multicut parameterized by the solution size and the number of cut requests. The result combines the matroid-based irrelevant edge approach used in the kernel for s-Multiway Cut with a recursive decomposition and sparsification of the graph along sparse cuts. This is the first progress on the kernelization of Multiway Cut problems since the kernel for s-Multiway Cut for constant value of s (Kratsch and Wahlström, FOCS 2012).

    References

    [1]
    Garima Agrawal and Soumen Maity. 2021. The small set vertex expansion problem. Theor. Comput. Sci. 886 (2021), 84–93. DOI:
    [2]
    Alexandr Andoni, Anupam Gupta, and Robert Krauthgamer. 2014. Towards \( 1 + \varepsilon \) -Approximate flow sparsifiers. In SODA. SIAM, 279–293. DOI:
    [3]
    Nikhil Bansal, Uriel Feige, Robert Krauthgamer, Konstantin Makarychev, Viswanath Nagarajan, Joseph Naor, and Roy Schwartz. 2014. Min-Max graph partitioning and small set expansion. SIAM J. Comput. 43, 2 (2014), 872–904. DOI:
    [4]
    Hans L. Bodlaender, Rodney G. Downey, Michael R. Fellows, and Danny Hermelin. 2009. On problems without polynomial kernels. J. Comput. Syst. Sci. 75, 8 (2009), 423–434. DOI:
    [5]
    Julia Chuzhoy. 2012. On vertex sparsifiers with Steiner nodes. In STOC. ACM, 673–688. DOI:
    [6]
    Marek Cygan, Fedor V. Fomin, Lukasz Kowalik, Daniel Lokshtanov, Dániel Marx, Marcin Pilipczuk, Michal Pilipczuk, and Saket Saurabh. 2015. Parameterized Algorithms. Springer. DOI:
    [7]
    Marek Cygan, Pawel Komosa, Daniel Lokshtanov, Marcin Pilipczuk, Michal Pilipczuk, Saket Saurabh, and Magnus Wahlström. 2021. Randomized contractions meet lean decompositions. ACM Trans. Algor. 17, 1 (2021), 6:1–6:30.
    [8]
    Elias Dahlhaus, David S. Johnson, Christos H. Papadimitriou, Paul D. Seymour, and Mihalis Yannakakis. 1994. The complexity of multiterminal cuts. SIAM J. Comput. 23, 4 (1994), 864–894.
    [9]
    Matthias Englert, Anupam Gupta, Robert Krauthgamer, Harald Räcke, Inbal Talgam-Cohen, and Kunal Talwar. 2014. Vertex sparsifiers: New results from old techniques. SIAM J. Comput. 43, 4 (2014), 1239–1262. DOI:
    [10]
    Fedor V. Fomin, Daniel Lokshtanov, Fahad Panolan, and Saket Saurabh. 2016. Efficient computation of representative families with applications in parameterized and exact algorithms. J. ACM 63, 4 (2016), 29:1–29:60. DOI:
    [11]
    Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh, and Meirav Zehavi. 2019. Kernelization: Theory of Parameterized Preprocessing. Cambridge University Press. DOI:
    [12]
    Lance Fortnow and Rahul Santhanam. 2011. Infeasibility of instance compression and succinct PCPs for NP. J. Comput. Syst. Sci. 77, 1 (2011), 91–106. DOI:
    [13]
    Naveen Garg, Vijay V. Vazirani, and Mihalis Yannakakis. 1996. Approximate max-flow min-(Multi)Cut theorems and their applications. SIAM J. Comput. 25, 2 (1996), 235–251. DOI:
    [14]
    Torben Hagerup, Jyrki Katajainen, Naomi Nishimura, and Prabhakar Ragde. 1998. Characterizing multiterminal flow networks and computing flows in networks of small treewidth. J. Comput. Syst. Sci. 57, 3 (1998), 366–375. DOI:
    [15]
    Eva-Maria C. Hols and Stefan Kratsch. 2018. A randomized polynomial kernel for subset feedback vertex set. Theor. Comput. Syst. 62, 1 (2018), 63–92. DOI:
    [16]
    Nikolai Karpov, Marcin Pilipczuk, and Anna Zych-Pawlewicz. 2019. An exponential lower bound for cut sparsifiers in planar graphs. Algorithmica 81, 10 (2019), 4029–4042. DOI:
    [17]
    Arindam Khan and Prasad Raghavendra. 2014. On mimicking networks representing minimum terminal cuts. Inf. Process. Lett. 114, 7 (2014), 365–371. DOI:
    [18]
    Stefan Kratsch. 2018. A randomized polynomial kernelization for vertex cover with a smaller parameter. SIAM J. Discrete Math. 32, 3 (2018), 1806–1839. DOI:
    [19]
    Stefan Kratsch and Magnus Wahlström. 2014. Compression via matroids: A randomized polynomial kernel for odd cycle transversal. ACM Trans. Algor. 10, 4 (2014), 20:1–20:15. DOI:
    [20]
    Stefan Kratsch and Magnus Wahlström. 2020. Representative sets and irrelevant vertices: New tools for kernelization. J. ACM 67, 3 (2020), 16:1–16:50. DOI:
    [21]
    Robert Krauthgamer and Havana Inbal Rika. 2020. Refined vertex sparsifiers of planar graphs. SIAM J. Discrete Math. 34, 1 (2020), 101–129. DOI:
    [22]
    Robert Krauthgamer and Inbal Rika. 2013. Mimicking networks and succinct representations of terminal cuts. In SODA. SIAM, 1789–1799. DOI:
    [23]
    Euiwoong Lee and Magnus Wahlström. 2020. LP-branching algorithms based on biased graphs. CoRR abs/1610. 06060v2 (2020).
    [24]
    Frank Thomson Leighton and Ankur Moitra. 2010. Extensions and limits to vertex sparsification. In STOC. ACM, 47–56. DOI:
    [25]
    Daniel Lokshtanov, Pranabendu Misra, Fahad Panolan, and Saket Saurabh. 2018. Deterministic truncation of linear matroids. ACM Trans. Algor. 14, 2 (2018), 14:1–14:20. DOI:
    [26]
    Anand Louis and Yury Makarychev. 2016. Approximation algorithms for hypergraph small-set expansion and small-set vertex expansion. Theor. Comput. 12, 1 (2016), 1–25. DOI:
    [27]
    Anand Louis, Prasad Raghavendra, and Santosh S. Vempala. 2013. The complexity of approximating vertex expansion. In FOCS. IEEE Computer Society, 360–369. DOI:
    [28]
    László Lovász. 1977. Flats in matroids and geometric graphs. In Proc. Sixth British Combinatorial Conf. (Combinatorial Surveys). 45–86.
    [29]
    Pasin Manurangsi. 2018. Inapproximability of maximum biclique problems, minimum k-Cut and densest at-least-k-Subgraph from the small set expansion hypothesis. Algorithms 11, 1 (2018), 10. DOI:
    [30]
    Dániel Marx. 2006. Parameterized graph separation problems. Theor. Comput. Sci. 351, 3 (2006), 394–406. DOI:
    [31]
    Dániel Marx. 2009. A parameterized view on matroid optimization problems. Theor. Comput. Sci. 410, 44 (2009), 4471–4479. DOI:
    [32]
    Ankur Moitra. 2009. Approximation algorithms for multicommodity-type problems with guarantees independent of the graph size. In FOCS. IEEE Computer Society, 3–12. DOI:
    [33]
    G. Nemhauser and L. Trotter. 1975. Vertex packing: Structural properties and algorithms. Math. Program. 8 (1975), 232–248. DOI:
    [34]
    James Oxley. 2011. Matroid Theory (2nd ed.). Oxford University Press. 2010936646
    [35]
    Harald Räcke. 2002. Minimizing congestion in general networks. In FOCS. IEEE Computer Society, 43–52. DOI:
    [36]
    Harald Räcke. 2008. Optimal hierarchical decompositions for congestion minimization in networks. In STOC. ACM, 255–264. DOI:
    [37]
    Prasad Raghavendra and David Steurer. 2010. Graph expansion and the unique games conjecture. In STOC. ACM, 755–764. DOI:
    [38]
    Prasad Raghavendra, David Steurer, and Madhur Tulsiani. 2012. Reductions between expansion problems. In Computational Complexity Conference. IEEE Computer Society, 64–73.
    [39]
    Felix Reidl and Magnus Wahlström. 2018. Parameterized algorithms for zero extension and metric labelling problems. In ICALP (LIPIcs), Vol. 107. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 94:1–94:14. DOI:
    [40]
    A. Schrijver. 2003. Combinatorial Optimization: Polyhedra and Efficiency. Springer.
    [41]
    Magnus Wahlström. 2017. LP-branching algorithms based on biased graphs. In SODA. SIAM, 1559–1570. DOI:
    [42]
    Magnus Wahlström. 2020. On quasipolynomial multicut-mimicking networks and kernelization of multiway cut problems. In ICALP (LIPIcs), Vol. 168. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 101:1–101:14. DOI:

    Cited By

    View all

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Algorithms
    ACM Transactions on Algorithms  Volume 18, Issue 2
    April 2022
    285 pages
    ISSN:1549-6325
    EISSN:1549-6333
    DOI:10.1145/3514175
    • Editor:
    • Edith Cohen
    Issue’s Table of Contents

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 04 March 2022
    Accepted: 01 November 2021
    Revised: 01 November 2021
    Received: 01 February 2021
    Published in TALG Volume 18, Issue 2

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Kernelization
    2. sparsification
    3. graph separation

    Qualifiers

    • Research-article
    • Refereed

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 174
      Total Downloads
    • Downloads (Last 12 months)73
    • Downloads (Last 6 weeks)7

    Other Metrics

    Citations

    Cited By

    View all

    View Options

    Get Access

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    Full Text

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media