摘要
Artificial intelligence(AI)and its applications have been a hot topic in recent years,while the optimization architecture is one of its key contents.In fact,typical AI models are often represented by minimizing or maximizing a certain objective/energy function.Therefore,an optimization model with a fast and robust algorithm is of great importance for AI.Traditional mathematical optimization models are based on human knowledge or physical hypothesis and have been playing a vital role in AI.We call them model-driven methods.Two model-driven papers are presented in this special issue to deal with the restoration and registration problems.With the development of machine learning algorithms and computational resources,data-driven methods have been becoming more and more useful in AI,due to their flexibility and efficiency for large-scale data.There are two data-driven papers presented in this issue to solve the classification and data-generating problems.
作者简介
Ya-Xin Peng,yaxin.peng@shu.edu.cn;Shao-Yi Du,dushaoyi@xjtu.edu.cn;Tie-Yong Zeng,zeng@math.cuhk.edu.hk。
