主講人簡(jiǎn)介：陳彩華，教授，國(guó)家優(yōu)秀青年基金(優(yōu)青)獲得者，南京大學(xué)理學(xué)博士，新加坡國(guó)立大學(xué)聯(lián)合培養(yǎng)博士，曾赴新加坡國(guó)立大學(xué)、香港中文大學(xué)等學(xué)習(xí)與訪問(wèn)。主持/完成的基金包括國(guó)家自然科學(xué)基金青年項(xiàng)目、面上項(xiàng)目和優(yōu)秀青年項(xiàng)目等，參與國(guó)家自然科學(xué)基金重點(diǎn)項(xiàng)目，代表作發(fā)表在《Mathematical Programming》，《SIAM Journal on Optimization》及CVPR、NIPS等國(guó)際知名學(xué)術(shù)期刊與會(huì)議, 多篇論文入選ESI高被引論文。獲華人數(shù)學(xué)家聯(lián)盟最佳論文獎(jiǎng)（2017、2018連續(xù)兩年），中國(guó)運(yùn)籌學(xué)會(huì)青年科技獎(jiǎng)（2018），南京大學(xué)青年五四獎(jiǎng)?wù)?2019)，入選首批南京大學(xué)仲英青年學(xué)者（全校10人，2018）及江蘇省社科優(yōu)青（2019）。
報(bào)告內(nèi)容概要：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.
南京大學(xué)陳彩華教授學(xué)術(shù)報(bào)告.docx
