报告题目：An efficient Hessian based algorithm for singly linearly and box constrained least squares regression
报告人：刘勇进 教授 福州大学
报告时间：2021年10月13日（周三）上午9：30 – 10：10
报告地点：腾讯会议 ID：503 718 277，会议密码：9999
校内联系人：李欣欣 xinxinli@jlu.edu.cn
报告摘要：The singly linearly and box constrained least squares regression has diverse applications in various fields. This paper proposes an efficient and robust semismooth Newton based augmented Lagrangian (SSNAL) algorithm for solving this problem, in which a semismooth Newton (SSN) algorithm with superlinear or even quadratic convergence is applied to solve the subproblems. Theoretically, the global and asymptotically superlinear local convergence of the SSNAL algorithm hold automatically under standard conditions. Computationally, a generalized Jacobian for the projector onto the feasible set is shown to be either diagonal or diagonal-minus-rank-1, which is a key ingredient for the efficiency of the SSNAL algorithm. Numerical experiments conducted on both synthetic and real data sets demonstrate that the SSNAL algorithm compared to several state-of-the-art first-order algorithms is much more efficient and robust.
报告人简介：刘勇进，福州大学数学与统计学院教授，博士生导师。研究兴趣主要包括：最优化理论、方法与应用，大规模数值计算，统计优化等，研究成果在Mathematical Programming, Series A、SIAM Journal on Optimization、SIAM Journal on Scientific Computing、Journal of Scientific Computing、Computational Optimization and Applications、Journal of Optimization Theory and Applications等国际优化学术期刊上发表。主持国家自然科学基金3项（面上项目2项、青年基金1项），主持其他省部级纵向科研项目5项。现任中国运筹学会学术交流委员会委员、中国运筹学会青年工作委员会委员、中国运筹学会数学规划分会理事、中国运筹学会智能工业数据解析与优化分会理事。
