应用数学青年讨论班（午餐会）—— Model-Based Derivative-Free Optimization
Derivative-free optimization concerns the optimization of black-box functions. It is a diverse field studied under multiple disciplines, which leads to an array of methods with fundamental distinctions. Among them, algorithms based on polynomial interpolation and trust-region methods are capable of finding solutions without the need for a large number of function queries, perfect for handling problems base on expensive computer simulations, while gradient descent methods coupled with finite differences are commonly used for solving machine learning problems such as reinforcement learning and neural adversarial attack. In this talk, Dr. Liyuan Cao presents his theoretical work on these two types of algorithms, including the analysis of approximation methods such as (randomized) finite differences and linear interpolation, and the analysis of adaptive optimization algorithms such as line search and trust-region methods under noise.
报告人简介： Liyuan Cao is a postdoctoral researcher at BICMR, Peking University under the supervision of Prof. Zaiwen Wen. He received his Ph.D. from the Industrial and Systems Engineering Department at Lehigh University in 2021 under Prof. Katya Scheinberg. His research focuses on the analysis and development of algorithms in nonlinear optimization and derivative-free optimization.
讨论班简介：北京大学应用数学青年讨论班 (Applied Mathematics Seminar for Youth) 是一个由北京大学卓越研究生计划组织的学术交流平台。该讨论班定期举办一系列读书会、学术报告，涵盖广泛的应用数学领域，旨在为应用数学领域的学生提供一个互相学习、交流和探讨的机会，促进学生们在该领域的学术成长和思维能力的培养。