Stochastic Enumeration Method for Counting Trees

Radislav Vaisman () and Dirk P. Kroese ()
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Radislav Vaisman: The University of Queensland
Dirk P. Kroese: The University of Queensland

Methodology and Computing in Applied Probability, 2017, vol. 19, issue 1, 31-73

Abstract: Abstract The problem of estimating the size of a backtrack tree is an important but hard problem in the computational sciences. An efficient solution of this problem can have a major impact on the hierarchy of complexity classes. The first randomized procedure, which repeatedly generates random paths through the tree, was introduced by Knuth. Unfortunately, as was noted by Knuth and a few other researchers, the estimator can introduce a large variance and become ineffective in the sense that it underestimates the cost of the tree. Recently, a new sequential algorithm called Stochastic Enumeration (SE) method was proposed by Rubinstein et al. The authors showed numerically that this simple algorithm can be very efficient for handling different counting problems, such as counting the number of satisfiability assignments and enumerating the number of perfect matchings in bipartite graphs. In this paper we introduce a rigorous analysis of SE and show that it results in significant variance reduction as compared to Knuth’s estimator. Moreover, we establish that for almost all random trees the SE algorithm is a fully polynomial time randomized approximation scheme (FPRAS) for the estimation of the overall tree size.

Keywords: Randomized algorithms; Monte Carlo sampling; Multilevel splitting; 05C05; 65C05; 05C85; 05C81; 60J80 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11009-015-9457-4

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