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电子下注平台青年学术论坛第197期——High-order multilevel optimization strategies and their application to the training of ANNs

发布者: [发表时间]:2019-03-28 [来源]: [浏览次数]:

主讲人:Serge Gratton教授

时间:2019年4月9日09:00-10:00

地点:主楼1214会议室

邀请人:孙聪

报告题目:High-order multilevel optimization strategies and their application to the training of ANNs

报告摘要:

Standard iterative optimization methods are based on the approximation of the objective function by a model, given by a truncated Taylor series. Models of order two are classically used. Recently, in the literature a unifying framework has been proposed, to extend the existing theory also to models of higher order. The use of such models comes along with higher costs. We propose a multilevel extension of such methods, to reduce the major cost per iteration, represented by the model minimization. The proposed methods rely on the knowledge of a sequence of approximations to the original objective function, defined on spaces of reduced dimension and cheaper to optimize. We also investigate the application of such techniques to problems where the variables are not related by geometrical constraints. We choose as an important representative of this class the training of artificial neural networks.

主讲人介绍:

Prof. Serge Gratton achieved his phD degree in Applied Mathematics in 1998 from University of Toulouse, France. He is now an exceptional full professor , and head of the Parallel Algorithm and Optimization (APO) team in INPT/ENSEEIHT, France. He is the associate editor of SIAM Otpimization, Optimization Methods and Softwares. His research interests include Theory and algorithms for constrained and unconstrained non-convex optimization, Data assimilation and filtering, Numerical linear algebra and High Performance Computing, and so on. He also developed several softwares such as CG and GMRES packages.