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【11th.June】ALAMO: Machine learning from data and first principles

2018-06-08


题目:ALAMO: Machine learning from data and first principles
演讲人:Nick Sahinidis教授,卡内基梅隆大学 化学工程系
时间:2018年6月11日(周一)9:00
地点:化学化工学院B楼(建工楼)410会议室
邀请人:贺益君 副研究员


摘要
We have developed the ALAMO methodology with the aim of producing a tool capable of using data to learn algebraic models that are accurate and as simple as possible.  ALAMO relies on (a) integer nonlinear optimization to build low-complexity models from input-output data, (b) derivative-free optimization to collect additional data points that can be used to improve tentative models, and (c) global optimization to enforce physical constraints on the mathematical structure of the model. We present computational results and comparisons between ALAMO and a variety of learning techniques, including Latin hypercube sampling, simple least-squares regression, and the lasso. We also describe results from applications in CO2 capture that motivated the development of ALAMO.


演讲人简介
Nick Sahinidis is the John E. Swearingen Professor and Director of the Center for Advanced Process Decision-making at Carnegie Mellon University.  He joined Carnegie Mellon in 2007 after a sixteen-year long career at the University of Illinois at Urbana, where he taught in Industrial Engineering and Chemical Engineering.  


His research has included the development of theory, algorithms, and the BARON software for global optimization of mixed-integer nonlinear programs. Professor Sahinidis’ research activities have been recognized by the INFORMS Computing Society Prize in 2004, the Beale-Orchard-Hays Prize from the Mathematical Programming Society in 2006, the Computing in Chemical Engineering Award in 2010, the Constantin Carathéodory Prize in 2015, and the National Award and Gold Medal from the Hellenic Operational Research Society in 2016.  Professor Sahinidis is a fellow of INFORMS and AIChE.  He is the current Editor-in-Chief of Optimization and Engineering.





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