Numerical Methods for Highly Nonlinear Stochastic Differential Equations

发布日期: 2018-03-12  作者:    浏览次数: 10 

报告题目:Numerical Methods for Highly Nonlinear Stochastic Differential Equations

报告人:University of Strathclyde 毛学荣 教授

报告时间:2018316日(星期五) 上午 10:00


报告摘要:This talk will begin with a review on the significant contributions of the paper by Higham, D.J., Mao, X. and Stuart, A.M. (Strong convergence of Euler-type methods for nonlinear stochastic differential equations, SIAM Journal on Numerical Analysis 40(3) (2002), 1041-1063.)This was the first to study the strong convergence of numerical solutions of SDEs under a local Lipschitz condition. Prior to this, all positive results were based on a much more restrictive global Lipschitz assumption, which rules out most realistic models. The field of numerical analysis of SDEs now has a very active research profile, much of which builds on the techniques developed in that paper, which has attracted 431 Google Scholar Citations. In particular, the theory developed there has formed the foundation for several recent very popular methods, including tamed Euler-Maruyama method and truncated Euler-Maruyama.The talk will show more details on the development of the truncated Euler-Maruyama.


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