學(xué)術(shù)報(bào)告
題目：A class of line search type methods for nonsmooth convex regularized minimization
報(bào)告人：周偉軍，長(zhǎng)沙理工大學(xué)，教授。
地點(diǎn)：騰訊會(huì)議（ID：568 460 974）
時(shí)間：2020年12月2日，14:40-16:30
摘要：This paper presents a class of line search type methods for solving the regularized optimization model whose objective function is the sum of a smooth function and a nonsmooth convex regularized term. This problem has many applications such as in compressive sensing and sparse reconstruction. Three special cases of this class of methods are proposed and their convergence theorems are established. They are generalizations of some existing BB gradient methods and PRP type nonlinear conjugate gradient methods for smooth unconstrained optimization problems. The proposed methods are applied to sparse reconstruction and some numerical results are reported to show their efficiency.
報(bào)告人簡(jiǎn)介
周偉軍，長(zhǎng)沙理工大學(xué)教授。2000、2003和2006年在湖南大學(xué)分別獲學(xué)術(shù)、碩士和博士學(xué)位，2007年在日本國(guó)立弘前大學(xué)訪(fǎng)問(wèn)一年，2008-2010年在香港理工大學(xué)進(jìn)行博士后工作。主要從事數(shù)值優(yōu)化研究，在Math. Comput.、SIAM J. Optim.等期刊發(fā)表論文20余篇，主持完成國(guó)家自科項(xiàng)目2項(xiàng)，獲省自然科學(xué)獎(jiǎng)二等獎(jiǎng)1項(xiàng)。
