2008
DOI: 10.1137/050644033
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Accelerating Simulated Annealing for the Permanent and Combinatorial Counting Problems

Abstract: We present an improved "cooling schedule" for simulated annealing algorithms for combinatorial counting problems. Under our new schedule the rate of cooling accelerates as the temperature decreases. Thus, fewer intermediate temperatures are needed as the simulated annealing algorithm moves from the high temperature (easy region) to the low temperature (difficult region). We present applications of our technique to colorings and the permanent (perfect matchings of bipartite graphs). Moreover, for the permanent,… Show more

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Cited by 77 publications
(123 citation statements)
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“…Monte Carlo method. Bezakova et al [19] improve this run-Indeed, when we apply our system-wide metric, we see that ning time to 0(nr7 log4 n) by using a new "cooling schedule" the bipartite graph has only a single perfect matching, thus for the simulated annealing algorithm running on top of the identifying the true communication pattern of the system. Markov chain.…”
Section: The Anonymity System Inmentioning
confidence: 84%
“…Monte Carlo method. Bezakova et al [19] improve this run-Indeed, when we apply our system-wide metric, we see that ning time to 0(nr7 log4 n) by using a new "cooling schedule" the bipartite graph has only a single perfect matching, thus for the simulated annealing algorithm running on top of the identifying the true communication pattern of the system. Markov chain.…”
Section: The Anonymity System Inmentioning
confidence: 84%
“…On the first point, approximation algorithms for the permanent of a matrix are a vigorously studied field of mathematics. Recent improvements include those of Jerrum et al (2004) [7], Bezáková et al (2006) [4], and Huber and Law (2008) [6]; the computational complexities of these methods are O ( n 10 (log n ) 3 ), O ( n 7 (log n ) 4 ), and O ( n 4 log n ), respectively. These dramatic decreases in complexity have been experienced only in the last ten years, and further developments can therefore be expected.…”
Section: Discussionmentioning
confidence: 99%
“…Exact computation of a permanent is #P-complete [3]. A #P class is a set of counting problems that belongs to nondeterministic polynomial time, and a problem is #P-complete if and only if it is in #P. Because it has been proved that the permanent is #P-complete, exact computation of the permanent is not possible in polynomial time, and algorithms for polynomial time approximation have therefore been proposed [4-7]. We employed the Huber–Law algorithm [6], which is, to the best of our knowledge, currently the fastest algorithm when the matrix is dense.…”
Section: Introductionmentioning
confidence: 99%
“…The normalisation is convenient if one wishes to compare the number of q-colourings in graphs on different numbers of vertices. Inequality (1) is the zero-temperature version of (2) F q G (β) ≤ F q K d,d (β) , since in the limit β → +∞, inequality (2) becomes exactly (1). For general d, Galvin [9,11] showed that (2) holds for all β when G is bipartite, and (1) holds for all d when q ≥ 2 d|V (G)|/2 4 .…”
Section: Introductionmentioning
confidence: 99%