报告题目：A Fast Data-Driven Iteratively Regularized Method with Convex Penalty for Solving Ill-Posed Problems

主 讲 人：郜广宇 副研究员（哈尔滨工业大学）

报告邀请人：谷瑞雪 副教授、傅红笋 教授

报 告 时间：2024年11月7日下午13:30-15:30

报告地点：腾讯会议 301-700-721

会议链接：https://meeting.tencent.com/dm/ILg6pK72nbsW

报告摘要：In this talk, we introduce a new iterative regularization method for solving inverse problems in Hilbert spaces. The iterative process of the proposed method combines classical iterative regularization format and Data-Driven approach. Data-Driven technique is based on the idea of deep learning to estimate the interior of a black box through a training set, so as to solve problems better and faster in some cases. In order to capture the special feature of solutions, convex functions are utilized to be penalty terms. Algorithmically, the two-point gradient acceleration strategy based on homotopy perturbation method is applied to the iterative scheme, which makes the method have satisfactory acceleration effect. We provide convergence analysis of the method under standard assumptions for iterative regularization methods. Finally, several numerical experiments are presented to show the effectiveness and acceleration effect of our method.

个人简历：郜广宇，哈尔滨工业大学副研究员。主要从事数学物理非线性反问题理论及其应用等方面的研究工作。在《Inverse Problems》、《SIAM Journal on Imaging Sciences》等国际知名期刊上发表多篇SCI论文，参与国家自然科学基金项目3项。