Seminars

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2019

Date Information
21 March 2019 Title: FBSDEs with discontinuous coefficients

Assistant Professor Ludovic Tangpi
Time: 4.00pm – 5.00pm 

Abstract

In this talk we consider well-posedness of systems of forward and backward stochastic differential equations when (at least some of) the coefficients are merely assumed to be measurable. Since such systems cannot be tackled with classical fixed point theory, we device new methods based on "domination arguments" and Malliavin calculus techniques.​

The talk is based on joint works with K. Bahlali and O. Menoukeu-Pamen​

​​Host: Nanyang Assistant Professor Ariel Neufeld​
Division of Mathematical Sciences, School of Physical and Mathematical Sciences​
12 February 2019  Title: Virtual element methods for elliptic variational inequalities of the second kind

Professor Huang Jianguo
Time: 4.00pm – 5.00pm

Abstract

In this talk, we are concerned with virtual element methods for solving elliptic variational inequalities (EVIs) of the second kind. First, a general framework is provided for the numerical solution of the EVIs and for its error analysis.  Then, two virtual element methods are applied to solve two representative EVIs: a simplified friction problem and a frictional contact problem, respectively. Optimal order error estimates are derived for the virtual element solutions of the two EVIs, including the effects of numerical integration for the non-smooth term in the EVIs.  A fast solver is introduced to solve the discrete problems. Several numerical examples are included to show the numerical performance of the proposed methods. This is a joint with Fang Feng from Shanghai Jiao Tong University and Weimin Han from University of Iowa.

Host:  Assistant Professor Tong Ping
Division of Mathematical Sciences, School of Physical and Mathematical Sciences
30 January 2019 Title: Analytical Nonlinear Shrinkage of Large-Dimensional Covariance Matrices  

Professor Michael Wolf 
Time: 3.30pm – 4.30pm

Abstract

We have proposed an analytical method for nonlinear shrinkage of covariance matrices based on kernel estimation in large dimensions. It enjoys the following merits:

1) Performs as well as existing methods

2) Easy to implement

3) Computationally cheap

4) More potential to accommodate future variations and extensions


Host: Associate Professor Pan Guangming
Division of Mathematical Sciences, School of Physical and Mathematical Sciences