新加坡科技設計大學林媚霞博士學術報告
報告人：林媚霞博士，新加坡科技設計大學工程系統(tǒng)與設計
報告題目：Exploring intrinsic structured sparsity in convex composite programming
報告時間：2022年11月25日，星期五，下午：14:00-15:00
報告地點：騰訊會議號：249 843 168; 密碼：1125
報告摘要：
Convex optimization models have been widely used in many applications such as machine learning and data science. However, the huge computation for the involved potentially large-scale problems has prevented their deployments in resource-limited devices. In our work, we design efficient second-order algorithms for the structured convex composite programming problems, which fully exploit the structure of the data and the underlying Hessians to highly reduce the computational cost. Dimension reduction techniques are also designed to further accelerate the computation, especially for the high-dimensional cases.
報告人簡介：
林媚霞，新加坡科技設計大學助理教授。2020年在新加坡國立大學數(shù)學系取得博士學位，2016年在南京大學取得信息計算與科學學士學位。主要研究興趣為開發(fā)與設計大數(shù)據(jù)科學中的模型與算法，特別是高效求解機器學習，統(tǒng)計估計和運籌學中涉及的超大規(guī)模優(yōu)化問題。以第一或通訊作者在高水平期刊和會議上發(fā)表多篇文章，包括SIAM Journal on Optimization, Mathematical Programming Computation, IEEE Transactions on Signal Processing及人工智能權(quán)威會議NIPS。
