题目一：Maximum principles for P1-P0 weak Galerkin finite element approximations of quasi-linear Second order elliptic equations

内容简介：In this talk, designing recovery algorithms for generalized linear models (GLMs) using approximate standard Bayesian inference algorithms (approximate message passing (AMP), vector approximate message passing (VAMP), sparse Bayesian learning (SBL), variational Bayesian inference (VBI)) will be presented. Substantial examples such as image classification, parameter estimation from quantized data and phase retrieval can be formulated as a GLM problem. Compared to the standard linear models (SLMs), solving the GLMs is more challenging because of the coupling of the linear and nonlinear transforms. Although the generalized approximate message passing (GAMP) algorithm has been proposed to solve the GLMs, it does not provide any insight into the relationship between the SLMs and GLMs. According to expectation propagation (EP), the GLM can be iteratively approximated as a sequence of SLM subproblems, and thus the standard Bayesian algorithm can be easily extended to solve the GLMs.

报告人：吉林大学  张然  教授

报告人简介：博士生导师。主要从事非标准有限元方法、随机微分方程数值解、多尺度分析及应用、金融衍生产品的数值计算等课题研究。在包括计算数学领域的重要期刊《SIAM J Numerical Analysis》、《SIAM J Scientific Computing》、《Mathematics of Computation》、《IMA J Numerical Analysis》等上发表学术论文50余篇。2013年，入选教育部新世纪人才奖励计划；2016年，入选“长江学者奖励计划”青年学者；2018年获国务院政府特殊津贴。

题目二：Relevant sampling in finitely generated shift-invariant spaces (II)

内容简介：We consider random sampling in finitely generated shift-invariant spaces $V(/Phi) /subset {/rm L}^2(/mathbb{R}^n)$ generated by a vector $/Phi = (/varphi_1,/ldots,/varphi_r) /in ( {/rm L}^2(/mathbb{R}^n))^r$. Following the approach introduced by Bass and Gr/"ochenig, we consider certain relatively compact subsets $V_{R,/delta}(/Phi)$ of such a space, defined in terms of a concentration inequality with respect to a cube with side lengths $R$. Under very mild assumptions on the generators, we show that for $R$ sufficiently large, taking $O(R^n log(R))$ many  random samples (taken independently uniformly distributed within $C_R$) yields  a sampling set for $V_{R,/delta}(/Phi)$ with high probability. We give explicit estimates of all involved constants in terms of the generators $/varphi_1, /ldots, /varphi_r$.

报告人：中山大学  冼军  教授

报告人简介：博士生导师、广东省千百十人才工程入选者、国家优秀青年基金获得者。2004年毕业于中山大学数学系获理学博士学位，同年进入浙江大学博士后流动站，2006年返回中山大学数学学院任副教授、教授、硕士研究生导师、博士研究生导师。主要研究方向为应用调和分析、采样理论及其在信号处理中的应用。2004年至今访问过美国耶鲁大学、美国中佛罗里达大学、加拿大Alberta大学，德国亚琛工业大学、法国马赛大学、新加坡国立大学、香港城市大学等高校，相关论文发表在APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS，JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS，BMC BIOINFORMATICS，SIGNAL PROCESSING，PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY，JOURNAL OF APPROXIMATION THEORY等国内外核心期刊。

时  间：2018年11月17日（周六）上午8：00始

地  点：南海楼330室

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