【北京应用物理与计算数学研究所】Recovery and Approximation of High Dimensional Data via Convex and Non-convex

报告题目：Recovery and Approximation of High Dimensional Data via Convex and Non-convex

报告人：谌稳固研究员 （北京应用物理与计算数学研究所）

报告时间：2020年9月4日 （星期五）下午3：00-4：00

腾讯会议 ID：274 544 544

报告摘要：In this talk, we consider the recovery conditions for the exact recovery of data with structures in the noiseless setting and approximation in the noisy case from incomplete information. The structure includes sparsity, the context when some prior information on the support of the signals is available. Moreover, we consider the optimality or sharpness of these sufficient conditions.

报告人简介：谌稳固，北京应用物理与计算数学研究所研究员，博士生导师，主要从事调和分析、非线性色散方程、大数据分析的理论及应用研究，在Applied and Computational Harmonic Analysis，IEEE Transactions on Information Theory, Inverse Problems, Signal Processing, Journal of Computational and Applied Mathematics，IEEE Signal Processing Letter, Inverse Problems and Imaging，CPDE, JDE, Nonlinear Analysis: Real World Applications等学术刊物发表科研论文60余篇。