Strong Convergence and Speed up of Nested Stochastic Simulation Algorithm
Strong Convergence and Speed up of Nested Stochastic Simulation Algorithm
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DOI：
10.4208/cicp.290313.051213s
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发表时间：
2014-04-01
影响因子：
3.7
通讯作者：
Liu, Di
中科院分区：
文献类型：
--
作者：
Huang, Can;Liu, Di
在本文中，我们首先证明了随机化学反应网络的嵌套随机模拟算法（NSSA）的强收敛性。然后，我们通过使用显式 Tau-Leaping 方法作为内求解器来近似快速过程的不变度量来研究算法的加速，为此也可以获得强误差估计。数值实验证明了我们分析的有效性。
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.