报告题目：Learning to be global optimizer

主讲人：西安交通大学孙建永教授

时间：2020年7月8日(周三) 10:00-11:00

地点：6-402会议室

主办单位：yh86银河国际

摘要：

The advancement of artificial intelligence has cast a new light on the development of optimization algorithm. This paper proposes to learn a two-phase (including a minimization phase and an escaping phase) global optimization algorithm for smooth non-convex functions. For the minimization phase, a model-driven deep learning method is developed to learn the update rule of descent direction, which is formalized as a nonlinear combination of historical information, for convex functions. We prove that the resultant algorithm with the proposed adaptive direction guarantees convergence for convex functions. Empirical study shows that the learned algorithm significantly outperforms some well-known classical optimization algorithms, such as gradient descent, conjugate descent and BFGS, and performs well on ill-posed functions. The escaping phase from local optimum is modeled as a Markov decision process with a fixed escaping policy. We prove that the fixed escaping policy is able to escape from local optimum with higher probability than random sampling. We further propose to learn an optimal escaping policy by reinforcement learning. The effectiveness of the escaping policies is verified by optimizing synthesized functions and training a deep neural network for CIFAR image classification. The learned two-phase global optimization algorithm demonstrates a promising global search capability on some benchmark functions and machine learning tasks.

主讲人简介：

孙建永，西安交通大学数学与统计学院教授、博士生导师、信息科学计算系系主任、院长助理、陕西国家应用数学中心常务副主任、陕西省数学会常务副理事长。主要研究方向包括统计机器学习、演化智能优化以及大数据的理论、算法与应用。已在美国科学院院刊（PNAS）和IEEE汇刊等顶级期刊上发表论文60余篇，谷歌学术引用超过2000余次，单篇最高引用超过330次。目前担任英国EPSRC/BBSRC审稿人，英国HEA Fellow，IEEE高级会员，中国计算机学会大数据专委会通讯委员，2016年获得第12批中组部-青年项目资助。多次受邀担任演化领域IEEE会议PC及高级程序委员。

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