A Discontinuous Galerkin Method for Approximating the Stationary Distribution of Stochastic Fluid-Fluid Processes

被引:0
作者
Nigel Bean
Angus Lewis
Giang T. Nguyen
Małgorzata M. O’Reilly
Vikram Sunkara
机构
[1] The University of Adelaide,School of Mathematical Sciences
[2] The University of Tasmania,Faculty of Science, Engineering, and Technology
[3] Freie Universität Berlin,Department of Mathematics and Computer Science
来源
Methodology and Computing in Applied Probability | 2022年 / 24卷
关键词
Stochastic fluid-fluid processes; Stationary distribution; Discontinuous Galerkin method;
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学科分类号
摘要
The stochastic fluid-fluid model (SFFM) is a Markov process {(Xt,Yt,φt),t≥0}\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{(X_t,Y_t,\varphi _t),t\ge 0\}$$\end{document}, where {φt,t≥0}\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{\varphi _t,{t\ge 0}\}$$\end{document} is a continuous-time Markov chain, the first fluid, {Xt,t≥0}\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{X_t,t\ge 0\}$$\end{document}, is a classical stochastic fluid process driven by {φt,t≥0}\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{\varphi _t,t\ge 0\}$$\end{document}, and the second fluid, {Yt,t≥0}\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{Y_t,t\ge 0\}$$\end{document}, is driven by the pair {(Xt,φt),t≥0}\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{(X_t,\varphi _t),t\ge 0\}$$\end{document}. Operator-analytic expressions for the stationary distribution of the SFFM, in terms of the infinitesimal generator of the process {(Xt,φt),t≥0}\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{(X_t,\varphi _t),t\ge 0\}$$\end{document}, are known. However, these operator-analytic expressions do not lend themselves to direct computation. In this paper the discontinuous Galerkin (DG) method is used to construct approximations to these operators, in the form of finite dimensional matrices, to enable computation. The DG approximations are used to construct approximations to the stationary distribution of the SFFM, and results are verified by simulation. The numerics demonstrate that the DG scheme can have a superior rate of convergence compared to other methods.
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页码:2823 / 2864
页数:41
相关论文
共 17 条
[1]  
Bean NG(2010)Quasi-birth-and-death processes with rational arrival process components Stoch Models 26 309-334
[2]  
Nielsen BF(2009)Algorithms for the Laplace-Stieltjes transforms of first return times for stochastic fluid flows Method Comput Appl Prob 10 381-408
[3]  
Bean NG(2013)A stochastic two-dimensional fluid model Stoch Models 29 31-63
[4]  
O’Reilly MM(2013)Spatially-coherent uniformization of a stochastic fluid model to a quasi-birth-and-death process Performance Evaluation 70 578-592
[5]  
Taylor PG(2014)The stochastic fluid-fluid model: A stochastic fluid model driven by an uncountable-state process, which is a stochastic fluid itself Stoch Proc Appl 124 1741-1772
[6]  
Bean NG(1995)Second-order fluid flow models: Reflected Brownian motion in a random environment Oper Res 43 77-88
[7]  
O’Reilly MM(2012)Wiener-Hopf factorizations for a multidimensional Markov additive process and their applications to reflected processes Stoch Syst 2 67-114
[8]  
Bean NG(2017)Stationary distributions for a class of Markov-modulated tandem fluid queues Stoch Models 33 524-550
[9]  
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[10]  
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