Stochastic and accelerated primal dual fixed point methods for large-scale data and image processing

：朱亚南（美国堪萨斯大学医学中心）
：2025-05-26 17:00
：海韵园行政楼C503

报 告 人：朱亚南（美国堪萨斯大学医学中心）

时  间：2025年5月26日17:00

地  点：海韵园行政楼C503

内容摘要:

Many important problems in data science and medical imaging—such as graphical lasso and computed tomography (CT) reconstruction—can be formulated as composite optimization problems. Although first-order primal-dual methods are widely adopted for their simplicity and low per-iteration cost, their performance often becomes unsatisfactory when applied to large-scale problems, which are increasingly common in practice. In this talk, we present three algorithmic extensions of the primal-dual fixed point (PDFP) method: the inertial PDFP (iPDFP), the stochastic PDFP (SPDFP), and the stochastic variance-reduced PDFP (SVRG-PDFP). These methods are specifically designed to address the computational challenges inherent in large-scale optimization tasks in data science and medical sciences. We provide convergence analyses for each algorithm and demonstrate their practical effectiveness through extensive numerical experiments.

个人简介：

朱亚南，美国堪萨斯大学医学中心助理教授。他于2021年获得上海交通大学数学科学学院与自然科学研究院计算数学博士学位，博士导师张小群教授。朱博士的研究兴趣主要集中于一阶优化算法以及其在医学数据科学，图像处理、生物医学工程和质子放射治疗等方向中的优化算法设计与应用。在《Journal of Scientific Computing》,《SIAM Journal on Imaging Sciences》,《SIAM Journal on Imaging Sciences》,《Medical Physics》,《IEEE Transactions on Biomedical Engineering》等期刊已发表论文十余篇。

 

联系人：赵状