題 目：Inertial Proximal Difference-of-Convex Algorithm with Convergent Bregman Plug-and-Play for Nonconvex Imaging
内容簡介：Imaging tasks are typically tackled using a structured optimization framework. This paper delves into a class of algorithms for difference-of-convex (DC) structured optimization, focusing on minimizing a DC function along with a possibly nonconvex function. Existing DC algorithm (DCA) versions often fail to effectively handle nonconvex functions or exhibit slow convergence rates. We propose a novel inertial proximal DC algorithm in Bregman geometry, named iBPDCA, designed to address nonconvex terms and enhance convergence speed through inertial techniques. We provide a detailed theoretical analysis, establishing both subsequential and global convergence of iBPDCA via the Kurdyka- Lojasiewicz property. Additionally, we introduce a Plug-and-Play variant, PnP-iBPDCA, which employs a deep neural network-based prior for greater flexibility and robustness while ensuring theoretical convergence. We also establish that the Gaussian gradient step denoiser used in our method is equivalent to evaluating the Bregman proximal operator for an implicitly weakly convex functional. We extensively validate our method on Rician noise and phase retrieval. We demonstrate that iBPDCA surpasses existing state-of-the-art methods.
報告人：吳中明
報告人簡介：南京信息工程大學副教授，香港中文大學博士後，新加坡國立大學訪問學者。研究方向為最優化算法及圖像應用。在SIAM Journal on Imaging Sciences, IEEE Transactions on Signal Processing, Mathematics of Computation, European Journal of Operational Research等期刊發表論文四十餘篇。入選南京信息工程大學首屆“青年科技之星”，江蘇省“雙創博士”，人社部“香江學者計劃”。擔任中國運籌學會宣傳工作委員會委員，中國運籌學會數學規劃分會青年理事，江蘇省運籌學會理事、副秘書長。主持國家自然科學基金面上、青年項目，江蘇省自然科學基金面上項目，教育部人文社科基金青年項目，中國博士後面上資助項目等。
時 間：2024年11月8日（周五）下午15：00開始
地 點：南海樓224室
熱烈歡迎廣大師生參加!
太阳集团1088vip