报告题目：Deep Block Proximal Linearized Minimization Algorithm for Nonconvex Inverse Problems
时 间：2026年1月22 日(星期四)16：00
地点：科研楼18号楼1102
主 办：数学与统计学院、分析数学及应用教育部重点实验室、福建省分析数学及应用重点实验室、统计学与人工智能福建省高校重点实验室、福建省应用数学中心（福建师范大学）
参加对象：感兴趣的老师和研究生
报告摘要：Image restoration is typically addressed through nonconvex inverse problems, which are often solved using first-order blockwise splitting methods. In this paper, we consider a general type of nonconvex optimization model that captures many inverse image problems and present an inertial block proximal linearized minimization (iBPLM) algorithm. Our new method unifies the Jacobi-type parallel and the Gauss--Seidel-type alternating update rules and extends beyond these approaches. The inertial technique is also incorporated into each blockwise subproblem update, which can accelerate numerical convergence. Furthermore, we extend this framework with a plug-and-play variant (PnP-iBPLM) that integrates deep gradient denoisers, offering a flexible and robust solution for complex imaging tasks. We provide comprehensive theoretical analysis, demonstrating both subsequential and global convergence of the proposed algorithms. To validate our methods, we apply them to multiblock dictionary learning problems in image denoising and deblurring. Experimental results show that both iBPLM and PnP-iBPLM significantly enhance numerical performance and robustness in these applications.
报告人简介：吴中明，南京信息工程大学教授，博士生导师，香港中文大学博士后，新加坡国立大学访问学者。曾入选人社部香江学者计划，江苏省科协青年托举工程，江苏省双创博士，获江苏省运筹学会首届青年科技奖。研究方向为运筹与优化。在SIIMS, SIMODS, IEEE TPAMI/TSP/TFS, EJOR, COAP, JoGO, MoC等期刊发表学术论文40余篇，授权国家发明专利3项。主持国家自然科学基金面上和青年项目，江苏省自然科学基金面上项目，教育部人文社科基金青年项目，中国博士后面上资助项目等。担任中国运筹学会竞赛工作委员会副秘书长，中国运筹学会数学规划分会和数学与智能分会青年理事，江苏省运筹学会理事、副秘书长。