【报告人】文有为教授

【报告题目】Selecting Regularization Parameters for Nuclear Norm Type Minimization Problems

【报告摘要】

（1）The reconstruction of low-rank matrix from its noisy observation finds its usage in many applications. It can be reformulated into a constrained nuclear norm minimization problem, where the bound $\eta$ of the constraint is explicitly given or can be estimated by the probability distribution of the noise. When the Lagrangian method is applied to find the minimizer, the solution can be obtained by the singular value thresholding operator where the thresholding parameter $\lambda$ is related to the Lagrangian multiplier.

（2）In this talk, we first show that the Frobenius norm of the discrepancy between the minimizer and the observed matrix is a strictly  increasing function of $\lambda$. From that we derive a closed-form solution for $\lambda$ in terms of $\eta$. The result can be used to solve the constrained nuclear-norm-type minimization problem when $\eta$ is given. For the unconstrained nuclear-norm-type regularized problems, our result allows us to automatically choose a suitable regularization parameter by using the discrepancy principle. The regularization parameters obtained are comparable to (and sometimes better than) those obtained by Stein's unbiased risk estimator (SURE) approach while the cost of solving the minimization problem can be reduced by 11--18 times. Numerical experiments with both synthetic data and real MRI data are performed to validate the proposed approach.

【报告时间】2022年6月27日，上午9：00——12：00

【报告形式】腾讯会议；会议号：741-267-399

【报告人简介】文有为，湖南师范大学数学与统计学院教授，博导，湖南省计算数学与应用软件学会副理事长。获香港大学博士学位，曾在新加坡国立大学、香港中文大学从事访问研究员、博士后等工作。主要研究方向为科学计算、数字图像处理与计算机视觉，在SIAM J. Sci. Comput., SIAM J. Imaging Sciences, Multiscale Model. Simul.，SIAM J. Matrix Anal., IEEE Trans. Image Process.等期刊发表论文30余篇，主持国家自然科学基金4项。以第一完成人身份，获2019年湖南省自然科学奖二等奖。