Abstract
In this paper we describe a Sequential Importance Sampling (SIS) procedure for counting the number of vertex covers in general graphs. The performance of SIS depends heavily on how close the SIS proposal distribution is to a uniform one over a suitably restricted set. The proposed algorithm introduces a probabilistic relaxation technique that uses Dynamic Programming in order to efficiently estimate this uniform distribution. The numerical experiments show that the scheme compares favorably with other existing methods. In particular the method is compared with cachet - an exact model counter, and the state of the art SampleSearch, which is based on Belief Networks and importance sampling.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.