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    Exploring intrinsic structured sparsity in convex composite programming

    2022-11-23  点击:[]

    新加坡科技设计大学林媚霞博士学术报告

    报告人:林媚霞博士,新加坡科技设计大学工程系统与设计

    报告题目:Exploring intrinsic structured sparsity in convex composite programming

    报告时间:20221125日,星期五,下午:14:00-15:00

    报告地点:腾讯会议号:249 843 168; 密码:1125

    报告摘要:

    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.

    报告人简介:

           林媚霞,新加坡科技设计大学助理教授。2020年在新加坡国立大学数学系取得博士学位,2016年在南京大学取得信息计算与科学学士学位。主要研究兴趣为开发与设计大数据科学中的模型与算法,特别是高效求解机器学习,统计估计和运筹学中涉及的超大规模优化问题。以第一或通讯作者在高水平期刊和会议上发表多篇文章,包括SIAM Journal on Optimization, Mathematical Programming Computation, IEEE Transactions on Signal Processing及人工智能权威会议NIPS。


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