High Dimensional Statistical Optimization

讲座简介

In this talk,I will explore this direction by focusing on sparse regression problems in high dimensions. A computational framework named iterative local adaptive majorize-minimization(I-LAMM) is proposed to simultaneously control algorithmic complexity and statisticalerror.   I-LAMM e?ectively turns the nonconvex penalized regression problem into a series of convex programs by utilizing the locally strong convexity of the problem when restricting the solution set in an l1 cone.

We propose a new column generation based algorithm that takes into account bounds on the gantry speed and dose rate, as well as an upper bound on the rate of change of the gantry speed, in addition to MLC constraints. 

时间

2017-06-30

10:00 ~ 11:45

主讲人

Qiang Sun, University of Toronto

地点

信息管理与工程学院308室
上海财经大学（第三教学楼西侧）
上海市杨浦区武东路100号