报告主题：高维结构化数据的优化算法
报告人：陶少哲 博士生 (美国明尼苏达大学)
报告时间：2017年5月10日（周三）14:00
报告地点：校本部G508
邀请人：白延琴
报告摘要：The talk will present my recent work in optimization methods for solving structured high dimension problems. In first part of the talk, we show all common first-order method, such as ISTA, FISTA, ADMM, coordinate descent, exhibit local linear convergence for LASSO problem. Using a spectral analysis, we show that, when close enough to the solution, FISTA slows down compared to ISTA, making it advantageous to switch to ISTA towards the end. In the second part, we propose a novel estimator for inverse covariance matrix with group structure. The problem can be efficiently solved via Frank-Wolfe method, leveraging chordal sparsity for scalability. Numerical results on synthetic and real datasets show significant improvement in sample complexity and performance.
欢迎教师、学生参加 ！