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Counting without sampling: new algorithms for enumeration problems using statistical physics
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Source Symposium on Discrete Algorithms archive
Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm table of contents
Miami, Florida
Pages: 890 - 899  
Year of Publication: 2006
ISBN:0-89871-605-5
Authors
Antar Bandyopadhyay  Chalmers University of Technology, Sweden
David Gamarnik  MIT Sloan School of Management, Cambridge, MA
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
: SIAM Activity Group on Discrete Mathematics
Publisher
ACM  New York, NY, USA
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ABSTRACT

We propose a new type of approximate counting algorithms for the problems of enumerating the number of independent sets and proper colorings in low degree graphs with large girth. Our algorithms are not based on a commonly used Markov chain technique, but rather are inspired by recent developments in statistical physics in connection with correlation decay properties of Gibbs measures and its implications to uniqueness of Gibbs measures on infinite trees, reconstruction problems and local weak convergence methods. On a negative side, our algorithms provide ∈-approximations only to the logarithms of the size of a feasible set (also known as free energy in statistical physics). But on the positive side, unlike Markov chain based algorithms, our approach provides deterministic as opposed to probabilistic guarantee on approximations. Moreover, for some regular graphs we obtain explicit values for the counting problem. For example, we show that every 4-regular n-node graph with large girth has asymptotically (1.494 ...)n independent sets, and in every r-regular graph with n nodes and large girth the number of qr + 1-proper colorings is asymptotically (q(1-1/q)r/2)n for large n. In statistical physics terminology, we compute explicitly the partition function (free energy) in these cases. We extend our results to random regular graphs graphs also. The explicit results obtained in this paper would be hard to derive via Markov chain sampling technique.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
{AB05} D. Aldous and A. Bandyopadhyay, A survey of max-type recursive distributional equations, Annals of Applied Probability 15 (2005), no. 2, 1047--1110.
 
2
 
3
{AS03} D. Aldous and J. M. Steele, The objective method: Probabilistic combinatorial optimization and local weak convergence, Discrete Combinatorial Probability, H. Kesten Ed., Springer-Verlag, 2003.
 
4
{Ban} A. Bandyopadhyay, Hard-core model on random graphs, In preparation.
 
5
{Ban02} A. Bandyopadhyay, Bivariate uniqueness in the logistic fixed point equation, Technical Report 629, Department of Statistics, UC, Berkeley (2002).
 
6
{BSVV} I. Bezakova, D. Stefankovic, V. Vazirani, and E. Vigoda, Improved simulated annealing algorithm for the permanent and combinatorial counting problems, Submitted.
 
7
{BW02} G. Brightwell and P. Winkler, Random colorings of a Cayley tree, in Contemporary Combinatorics, B. Bollobas, ed., Bolyai Society Mathematical Studies, 2002, pp. 247--276.
 
8
9
 
10
 
11
 
12
{Dob70} R. L. Dobrushin, Prescribing a system of random variables by the help of conditional distributions, Theory of Probability and its Applications 15 (1970), 469--497.
 
13
{Gam04} D. Gamarnik, Linear phase transition in random linear constraint satisfaction problems, Probability Theory and Related Fields. 129 (2004), no. 3, 410--440.
 
14
{Geo88} H. O. Georgii, Gibbs measures and phase transitions, de Gruyter Studies in Mathematics 9, Walter de Gruyter & Co., Berlin, 1988.
 
15
 
16
{GNSb} D. Gamarnik, T. Nowicki, and G. Swirszcz, Dynamics of exponential linear map in functional space, Submitted.
 
17
{JLR00} S. Janson, T. Łuczak, and A. Rucinski, Random graphs, John Wiley and Sons, Inc., 2000.
 
18
{Jon02} J. Jonasson, Uniqueness of uniform random colorings of regular trees, Statistics and Probability Letters 57 (2002), 243--248.
 
19
 
20
21
 
22
{Kel85} F. Kelly, Stochastic models of computer communication systems, J. R. Statist. Soc. B 47 (1985), no. 3, 379--395.
 
23
24
 
25
 
26
{Mos04} E. Mossel, Survey: information flow on trees, J. Nestril and P. Winkler, editors. Graphs, Morphisms and Statistical Physiscs. DIMACS series in discrete mathematics and theoretical computer science. American Mathematical Society., 2004, pp. 155--170.
 
27
{MP05} M. Mezard and G. Parisi, The cavity method at zero temperature, http://fr.arxiv.org/ps/cond-mat/0207121 (2005).
 
28
{MPV87} M. Mezard, G. Parisi, and M. A. Virasoro, Spin-glass theory and beyond, vol 9 of Lecture Notes in Physics, World Scientific, Singapore, 1987.
 
29
{RBMM04} O. Rivoire, G. Biroli, O. C. Martin, and M. Mezard, Glass models on Bethe lattices, Eur. Phys. J. B 37 (2004), 55--78.
 
30
{Tal01} M. Talagrand, The high temperature case of the K-sat problem, Probability Theory and Related Fields 119 (2001), 187--212.
 
31
{TAl03} M. Talagrand, Parisi formula, Ann. of Mathematics, to apper (2003).
 
32
{Val79} L. G. Valiant, The complexity of computing the permanent, Theoretical computer science 8 (1979), 189--201.
 
33
{War05} J. Warren, Dynamics and endogeny for recursive processes on trees, http://arxiv.org/abs/math.PR/0506038 (2005).
 
34


Collaborative Colleagues:
Antar Bandyopadhyay: colleagues
David Gamarnik: colleagues