Learn to optimize—a brief overview  

作　　者：Ke Tang Xin Yao 

机构地区：[1]Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,China [2]Department of Computing and Decision Sciences,Lingnan University,Hong Kong 999077,China

出　　处：《National Science Review》2024年第8期33-41,共9页国家科学评论（英文版）

基　　金：supported by the National Key Research and Development Program of China (2022YFA1004102);the National Natural Science Foundation of China (62250710682);the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2017ZT07X386).

摘　　要：Most optimization problems of practical significance are typically solved by highly configurable parameterized algorithms.To achieve the best performance on a problem instance,a trial-and-error configuration process is required,which is very costly and even prohibitive for problems that are already computationally intensive,e.g.optimization problems associated with machine learning tasks.In the past decades,many studies have been conducted to accelerate the tedious configuration process by learning from a set oftraining instances.This article refers to these studies as learn to optimize and reviews the progress achieved.

关 键 词：optimization data-driven algorithm design automated algorithm configuration machine learning 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]