Strong Convergence and Speed up of Nested Stochastic Simulation Algorithm
Can Huang 1, Di Liu 1*1 Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA.
Received 29 March 2013; Accepted (in revised version) 5 December 2013
Available online 21 January 2014
In this paper, we revisit the Nested Stochastic Simulation Algorithm (NSSA) for stochastic chemical reacting networks by first proving its strong convergence. We then study a speed up of the algorithm by using the explicit Tau-Leaping method as the Inner solver to approximate invariant measures of fast processes, for which strong error estimates can also be obtained. Numerical experiments are presented to demonstrate the validity of our analysis.AMS subject classifications: 65C30, 60H35
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Key words: Stochastic simulation algorithm, biochemical reacting network, strong convergence.
Email: email@example.com (C. Huang), firstname.lastname@example.org (D. Liu)