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学术报告报告人:李继彬教授(浙江师范大学)题目： Bifurcations and Exact Travelling Wave Solutions of the Generalized Two-Component Camassa-Holm Equation    时间：2013年10月22日(星期二)下午3：00—4：00地点：致远楼102欢迎各位参加

学术报告报告人:韩茂安教授(上海师范大学)题目：The stability of some kinds of generalized homoclinic loops in planar piecewise smooth systems时间：2013年10月22日(星期二)下午4：00—5：00地点：致远楼102欢迎各位参加

Development of efficient solvers for solving linear systems Ax = b is one of the most significant issue on the field of computational science and engineering. The GMRES method of Saad and Schultz is a well-known solver as the typical Krylov subspace method for nonsymmetric linear systems. While the GMRES method show good convergence, it has difficulties in storage and computational costs. To remedy these difficulties, the restart technique is generally used as a practical choice. The restarted version of the GMRES method is called the GMRES(m) method. In this talk, we will focus on a relationship between the GMRES(m) method and the error equation and propose a new variant of the GMRES(m) method. Several numerical results will show that this new variant will be attractive.

题 目：发展方程的谱元法及其并行化报告人：上海大学数学系学术委员会主任、教学指导委员会主任、博士生导师马和平 教授时间：2013年10月15日（星期二）下午3：00-3:50地点：数学系102

题 目：Spectral element method for fourth order报告人：上海师范大学数理信息学院、名誉 院长、博士生导师郭本瑜 教授时间：2013年10月15日（星期二）下午4：00-5:00地点：数学系102

我们讨论了OU型过程的三个统计推断问题，一是OU型过程的参数估计问题，在参数估计问题中，我们讨论了伽马OU型过程的最大似然估计、一般OU型过程的最大经验似然估计和基于连续矩的GM估计；二是检验二个OU型过程分布是否相等的假设检验问题；三是OU型过程的变点检验问题。时间：2013年10月15日（周二）下午14:30开始地点：数学系致远楼107

We propose a two-step variable selection procedure for high dimensional fixed censored quantile regression, in which the response subject to fixed censoring, and the dimension of the covariates, is very large, possibly much larger than the sample size . To account for fixed censoring data with multivariate covariates, we employ the ideas of informative subset-based estimator (Tang et el. (2012)) and effective dimension reduction. Under some regularity conditions, we show our procedure enjoys the model selection consistency, as long as the conditional censoring probability can be estimated consistently. We demonstrate that the first step penalized estimator with LASSO penalty can reduces the model from ultra-high dimensional to a model whose size has the same order with the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying adaptive LASSO penalty to the reduced model obtained from the first step.

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