Abstract: The sparse representation theory and non-convex optimization play important roles in compressed sensing and machine learning. In this talk, I will focus on the sparse decomposition of signals and some applications in recovery of sparse signals via non-convex minimization.

 

Bio: 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、压缩感知、机器学习、大数据分析的理论及应用研究，在IEEE Transactions on Information Theory, Applied and Computational Harmonic Analysis，Inverse Problems, SIAM Journal on Imaging Sciences, Journal of Computational Physics等学术期刊发表科研论文80余篇。