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Mean-square approximations of Levy noise driven SDEs with super-linearly growing diffusion and jump coefficients
时间:2021年03月27日 12:49 点击数:

报告人:甘四清

报告地点:腾讯会议

报告时间:2021年03月29日星期一15:00-16:00

邀请人:李晓月

报告摘要:

We first establish a fundamental mean-square convergence theorem for general one-step numerical approximations of Levy noise driven stochastic differential equations with non-globally Lipschitz coefficients. Then two novel explicit schemes are designed and their convergence rates are exactly identified via the fundamental theorem. Different from existing works, we do not impose a globally Lipschitz condition on the jump coefficient but formulate appropriate assumptions to allow for its super-linear growth. However, we require that the Levy measure is finite. New arguments are developed to handle essential difficulties in the convergence analysis, caused by the superlinear growth of the jump coefficient and the fact that higher moment bounds of the Poisson increments contribute to magnitude not more than . Numerical results are finally reported to confirm the theoretical findings.

会议网址:https://meeting.tencent.com/s/e7BUvVMDTasR

会议ID:947872385

会议密码:123456


 

主讲人简介:

甘四清,博士,中南大学教授,博士生导师,2001年毕业于中国科学院数学研究所,获理学博士学位,2001-2003年在清华大学计算机科学与技术系高性能计算研究所做博士后,曾先后访问美国、新加坡、香港等科研院所。主要研究方向为确定性微分方程和随机微分方程数值解法。主持国家自然科学基金面上项目4项, 参加国家自然科学基金重大研究计划集成项目1项。在 SIAM Journal on Scientific Computing、Journal of Scientific Computing、BIT Numerical Analysis、Applied Numerical Mathematics、 《中国科学》等国内外学术刊物上发表论文90余篇。2005年入选湖南省首批新世纪121人才工程。2014年湖南省优秀博士学位论文指导老师。

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