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    1. 數學系Seminar第2042期 Algorithmic Design for Big Data Related Optimization

      創建時間:  2020/11/05  龔惠英   瀏覽次數:   返回

      報告主題:Algorithmic Design for Big Data Related Optimization

      報告人:陳彩華 副教授 (南京大學)

      報告時間:2020年11月6日(周五) 15:00

      報告地點:G507

      邀請人:徐姿

      主辦部門:理學院數學系

      報告摘要:We live in the age of big data. The 5 characteristics of big data - volume, value, variety, velocity and veracity - have a significant impact on optimization. In this talk, we discuss some thinking of algorithmic design for big data related optimization problems. Specifically, we consider splitting methods for large scale structure optimization, to analyze the data with high volume and low value density. We also design efficient algorithms for distribution robust optimization, to cope with brittle veracity in data analysis. Finally, we propose LP-based approach for Markov Decision Process, which lays a deep ground in sequential decision making with dynamic data generated at a high velocity.


      歡迎教師、學生參加!

      上一條:數學系Seminar第2043期 Isomorphism between the R-matrix and Drinfeld presentations of quantum affine algebras

      下一條:數學系Seminar第2041期 Massive Random Access for 5G and Beyond: An Optimization Perspective


      數學系Seminar第2042期 Algorithmic Design for Big Data Related Optimization

      創建時間:  2020/11/05  龔惠英   瀏覽次數:   返回

      報告主題:Algorithmic Design for Big Data Related Optimization

      報告人:陳彩華 副教授 (南京大學)

      報告時間:2020年11月6日(周五) 15:00

      報告地點:G507

      邀請人:徐姿

      主辦部門:理學院數學系

      報告摘要:We live in the age of big data. The 5 characteristics of big data - volume, value, variety, velocity and veracity - have a significant impact on optimization. In this talk, we discuss some thinking of algorithmic design for big data related optimization problems. Specifically, we consider splitting methods for large scale structure optimization, to analyze the data with high volume and low value density. We also design efficient algorithms for distribution robust optimization, to cope with brittle veracity in data analysis. Finally, we propose LP-based approach for Markov Decision Process, which lays a deep ground in sequential decision making with dynamic data generated at a high velocity.


      歡迎教師、學生參加!

      上一條:數學系Seminar第2043期 Isomorphism between the R-matrix and Drinfeld presentations of quantum affine algebras

      下一條:數學系Seminar第2041期 Massive Random Access for 5G and Beyond: An Optimization Perspective

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