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

Approximate Counting, the Lovász Local Lemma, and Inference in Graphical Models

Published: 05 April 2019 Publication History

Abstract

In this article, we introduce a new approach to approximate counting in bounded degree systems with higher-order constraints. Our main result is an algorithm to approximately count the number of solutions to a CNF formula Φ when the width is logarithmic in the maximum degree. This closes an exponential gap between the known upper and lower bounds.
Moreover, our algorithm extends straightforwardly to approximate sampling, which shows that under Lovász Local Lemma-like conditions it is not only possible to find a satisfying assignment, it is also possible to generate one approximately uniformly at random from the set of all satisfying assignments. Our approach is a significant departure from earlier techniques in approximate counting, and is based on a framework to bootstrap an oracle for computing marginal probabilities on individual variables. Finally, we give an application of our results to show that it is algorithmically possible to sample from the posterior distribution in an interesting class of graphical models.

References

[1]
D. Achlioptas and F. Iliopoulos. 2016. Random walks that find perfect objects and the Lovász Local Lemma. J. ACM 63, 3 (2016), 22.
[2]
N. Alon. 1991. A parallel algorithmic version of the local lemma. Rand. Struct. Alg. 2, 4 (1991), 367--378.
[3]
J. Beck, 1991. An algorithmic approach to the Lovász Local Lemma. Rand. Struct. Alg. 2, 4 (1991), 343--365.
[4]
I. Bezákova, A. Galanis, L. A. Goldberg, H. Guo, and D. Stefankovi&cbreve;. 2016. Approximation via correlation decay when strong spatial mixing fails. In Proceedings of the International Colloquium on Automata, Languages, and Programming (ICALP’16), 1--13.
[5]
M. Bordewich, M. Dyer, and M. Karpinski. 2006. Stopping times, metrics, and approximate counting. In Proceedings of the International Colloquium on Automata, Languages, and Programming (ICALP’06), 108--119.
[6]
G. Bresler, E. Mossel, and A. Sly. 2013. Reconstruction of Markov random fields from samples: Some observations and algorithms. SIAM J. Comput. 42, 2 (2013), 563--578.
[7]
K. Chandrasekaran, N. Goyal, and B. Haeupler. 2013. Deterministic algorithms for the Lovász Local Lemma. SIAM J. Comput. 42, 6 (2013), 2132--2155.
[8]
P. Dagum and M. Luby. 1993. Approximating probabilistic inference in Bayesian belief networks is N P-hard. Artific. Intel. 60 (1993), 141--153.
[9]
P. Dagum and M. Luby. 1997. An optimal approximation algorithm for Bayesian inference. Artific. Intel. 93, 1-2 (1997), 1--27.
[10]
P. Erdös and L. Lovász. 1975. Problems and results on 3-chromatic hypergraphs and some related questions. In Infinite and Finite Sets. North Holland, 609--627.
[11]
A. Galanis, D. Stefankovi&cbreve;, and E. Vigoda. 2016. Inapproximability of the partition function for the antiferromagnetic Ising and hard-core models. Combin., Prob. Comput. 25, 4 (2016), 500--559.
[12]
D. Gamarnik and D. Katz. 2012. Correlation decay and deterministic FPTAS for counting colorings of a graph. J . Disc. Alg. 12 (2012), 29--47.
[13]
H. Guo, M. Jerrum, and J. Liu. 2017. Uniform sampling through the Lovász Local Lemma. In Proceedings of the ACM Symposium on Theory of Computing (STOC’17), 342--355.
[14]
H. Guo, C. Liao, P. Lu, and C. Zhang. 2018. Counting hypergraph colourings in the local lemma regime. In Proceedings of the ACM Symposium on Theory of Computing (STOC’18), 926--939.
[15]
B. Haeupler, B. Saha, and A. Srinivasan. 2011. New constructive aspects of the Lovász Local Lemma. In J. ACM 58, 6 (2011), 28.
[16]
D. Harris and A. Srinivasan. 2017. A constructive algorithm for the Lovász Local Lemma on permutations. In Theor . Comput. 13, 1 (2017), 1--41.
[17]
N. Harvey, P. Srivastava, and J. Vondrák. 2018. Computing the independence polynomial in Shearer’s region for the LLL. In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA’18), 1557--1576.
[18]
N. Harvey and J. Vondrák. 2015. An algorithmic proof of the Lovász Local Lemma via resampling oracles. In Proceedings of the IEEE Symposium on Foundations of Computer Science (FOCS’15), 1327--1346.
[19]
J. Hermon, A. Sly, and Y. Zhang. 2016. Rapid mixing of hypergraph independent set. ArXiv:1610.07999.
[20]
M. Jerrum, L. Valiant, and V. Vazirani. 1986. Random generation of combinatorial structures from a uniform distribution. Theoret. Comput. Sci. 43 (1986), 169--188.
[21]
D. Knuth. 1969. The Art of Computer Programming, Vol I. Addison Wesley, London, p. 396 (exercise 11).
[22]
V. Kolmogorov. 2016. Commutativity in the algorithmic Lovász Local Lemma. In Proceedings of the IEEE Symposium on Foundations of Computer Science (FOCS’16), to appear.
[23]
J. Liu and P. Lu. 2015. FPTAS for counting monotone CNF. In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA’15), 1531--1548.
[24]
R. Moser and G. Tardos. 2010. A constructive proof of the Lovász Local Lemma. J. ACM 57, 2 (2010), 1--15.
[25]
C. Nair and P. Tetali. 2007. The correlation decay (CD) tree and strong spatial mixing in multi-spin systems. ArXiv:0701494.
[26]
A. Sinclair and M. Jerrum. 1989. Approximately counting, uniform generation and rapidly mixing Markov chains. Inform. Comput. 82 (1989), 93--133.
[27]
A. Sinclair, P. Srivastava, and M. Thurley. 2014. Approximation algorithms for two-state anti-ferromagnetic spin systems. J. Stat. Phys. 155, 4 (2014), 666--686.
[28]
A. Sly. 2010. Computational transition at the uniqueness threshold. In Proceedings of the IEEE Symposium on Foundations of Computer Science (FOCS’10), 287--296.
[29]
A. Sly and N. Sun. 2014. Counting in two-spin models on d-regular graphs. Ann. Prob. 42, 6 (2014), 2383--2416.
[30]
D. Weitz. 2006. Counting independent sets up to the tree threshold. In Proceedings of the ACM Symposium on Theory of Computing (STOC’06), 140--149.

Cited By

View all
  • (2024)Fast Sampling of Satisfying Assignments from Random \(\boldsymbol{k}\)-SAT with Applications to ConnectivitySIAM Journal on Discrete Mathematics10.1137/23M159572238:4(2750-2811)Online publication date: 30-Oct-2024
  • (2023)Towards derandomising Markov chain Monte Carlo2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS57990.2023.00120(1963-1990)Online publication date: 6-Nov-2023
  • (2023)On the zeroes of hypergraph independence polynomialsCombinatorics, Probability and Computing10.1017/S0963548323000330(1-20)Online publication date: 21-Sep-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Journal of the ACM
Journal of the ACM  Volume 66, Issue 2
April 2019
260 pages
ISSN:0004-5411
EISSN:1557-735X
DOI:10.1145/3318168
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 April 2019
Accepted: 01 November 2018
Revised: 01 October 2018
Received: 01 November 2017
Published in JACM Volume 66, Issue 2

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Approximate counting
  2. Lovász local lemma
  3. graphical models

Qualifiers

  • Research-article
  • Research
  • Refereed

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)163
  • Downloads (Last 6 weeks)40
Reflects downloads up to 10 Nov 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Fast Sampling of Satisfying Assignments from Random \(\boldsymbol{k}\)-SAT with Applications to ConnectivitySIAM Journal on Discrete Mathematics10.1137/23M159572238:4(2750-2811)Online publication date: 30-Oct-2024
  • (2023)Towards derandomising Markov chain Monte Carlo2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS57990.2023.00120(1963-1990)Online publication date: 6-Nov-2023
  • (2023)On the zeroes of hypergraph independence polynomialsCombinatorics, Probability and Computing10.1017/S0963548323000330(1-20)Online publication date: 21-Sep-2023
  • (2023)Implementations and the independent set polynomial below the Shearer thresholdTheoretical Computer Science10.1016/j.tcs.2022.10.025939(194-215)Online publication date: Jan-2023
  • (2022)Belief propagation on the random k-SAT modelThe Annals of Applied Probability10.1214/21-AAP177232:5Online publication date: 1-Oct-2022
  • (2022)Inapproximability of Counting Hypergraph ColouringsACM Transactions on Computation Theory10.1145/355855414:3-4(1-33)Online publication date: 2-Sep-2022
  • (2022)Simple parallel algorithms for single-site dynamicsProceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing10.1145/3519935.3519999(1431-1444)Online publication date: 9-Jun-2022
  • (2022)Tight Lipschitz Hardness for optimizing Mean Field Spin Glasses2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS54457.2022.00037(312-322)Online publication date: Oct-2022
  • (2022)Sampling Lovász local lemma for general constraint satisfaction solutions in near-linear time2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS54457.2022.00021(147-158)Online publication date: Oct-2022
  • (2022)Towards the sampling Lovász Local Lemma2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS52979.2021.00025(173-183)Online publication date: Feb-2022
  • Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Get Access

Login options

Full Access

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media