TY - GEN
T1 - Sublinear-time algorithms for monomer-dimer systems on bounded degree graphs
AU - Lelarge, Marc
AU - Zhou, Hang
PY - 2013/12/1
Y1 - 2013/12/1
N2 - For a graph G, let Z(G,λ) be the partition function of the monomer-dimer system defined by Σk mk (G)λk, where mk(G) is the number of matchings of size k in G. We consider graphs of bounded degree and develop a sublinear-time algorithm for estimating log Z(G,λ) at an arbitrary value λ > 0 within additive error εn with high probability. The query complexity of our algorithm does not depend on the size of G and is polynomial in 1/ε, and we also provide a lower bound quadratic in 1/ε for this problem. This is the first analysis of a sublinear-time approximation algorithm for a # P-complete problem. Our approach is based on the correlation decay of the Gibbs distribution associated with Z(G,λ). We show that our algorithm approximates the probability for a vertex to be covered by a matching, sampled according to this Gibbs distribution, in a near-optimal sublinear time. We extend our results to approximate the average size and the entropy of such a matching within an additive error with high probability, where again the query complexity is polynomial in 1/ε and the lower bound is quadratic in 1/ε. Our algorithms are simple to implement and of practical use when dealing with massive datasets. Our results extend to other systems where the correlation decay is known to hold as for the independent set problem up to the critical activity.
AB - For a graph G, let Z(G,λ) be the partition function of the monomer-dimer system defined by Σk mk (G)λk, where mk(G) is the number of matchings of size k in G. We consider graphs of bounded degree and develop a sublinear-time algorithm for estimating log Z(G,λ) at an arbitrary value λ > 0 within additive error εn with high probability. The query complexity of our algorithm does not depend on the size of G and is polynomial in 1/ε, and we also provide a lower bound quadratic in 1/ε for this problem. This is the first analysis of a sublinear-time approximation algorithm for a # P-complete problem. Our approach is based on the correlation decay of the Gibbs distribution associated with Z(G,λ). We show that our algorithm approximates the probability for a vertex to be covered by a matching, sampled according to this Gibbs distribution, in a near-optimal sublinear time. We extend our results to approximate the average size and the entropy of such a matching within an additive error with high probability, where again the query complexity is polynomial in 1/ε and the lower bound is quadratic in 1/ε. Our algorithms are simple to implement and of practical use when dealing with massive datasets. Our results extend to other systems where the correlation decay is known to hold as for the independent set problem up to the critical activity.
UR - https://www.scopus.com/pages/publications/84893364358
U2 - 10.1007/978-3-642-45030-3_14
DO - 10.1007/978-3-642-45030-3_14
M3 - Conference contribution
AN - SCOPUS:84893364358
SN - 9783642450297
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 141
EP - 151
BT - Algorithms and Computation - 24th International Symposium, ISAAC 2013, Proceedings
T2 - 24th International Symposium on Algorithms and Computation, ISAAC 2013
Y2 - 16 December 2013 through 18 December 2013
ER -