The decomposition of a blocking model for connection-oriented networks

Two general-purpose decomposition methods to calculate the blocking probabilities of connection-oriented networks are presented. The methods are based on either the call status or the link status of the networks, and can significantly reduce the required computational times. A heuristic is presented to simplify the application of the proposed decomposition methods on networks with irregular topologies. Numerical examples are given to demonstrate the applications of the proposed methods. © 2004 IEEE.published_or_final_versio

Event-chain Monte Carlo: foundations, applications, and prospects

This review treats the mathematical and algorithmic foundations of non-reversible Markov chains in the context of event-chain Monte Carlo (ECMC), a continuous-time lifted Markov chain that employs the factorized Metropolis algorithm. It analyzes a number of model applications, and then reviews the formulation as well as the performance of ECMC in key models in statistical physics. Finally, the review reports on an ongoing initiative to apply the method to the sampling problem in molecular simulation, that is, to real-world models of peptides, proteins, and polymers in aqueous solution.Comment: 35 pages, no figure