Nonlinear optimization and support vector machines
本文聚焦监督二分类问题，介绍支持向量机背后的凸规划问题，分析其训练中最重要的优化方法，并讨论如何利用问题特性设计高效算法。
Abstract Support vector machine (SVM) is one of the most important class of machine learning models and algorithms, and has been successfully applied in various fields. Nonlinear optimization plays a crucial role in SVM methodology, both in defining the machine learning models and in designing convergent and efficient algorithms for large-scale training problems. In this paper we present the convex programming problems underlying SVM focusing on supervised binary classification. We analyze the most important and used optimization methods for SVM training problems, and we discuss how the properties of these problems can be incorporated in designing useful algorithms.