A sparse semismooth Newton based augmented Lagrangian method for large-scale support vector machines

TL;DR

This paper introduces a novel sparse semismooth Newton augmented Lagrangian method tailored for large-scale support vector machines, significantly improving efficiency and convergence in high-dimensional, large-sample scenarios.

Contribution

It develops a specialized solver exploiting problem sparsity and structure, achieving faster convergence for large-scale SVM dual problems compared to existing methods.

Findings

Outperforms state-of-the-art solvers in large-scale SVMs

Leverages second-order sparsity for efficiency

Guarantees fast local convergence rate

Abstract

Support vector machines (SVMs) are successful modeling and prediction tools with a variety of applications. Previous work has demonstrated the superiority of the SVMs in dealing with the high dimensional, low sample size problems. However, the numerical difficulties of the SVMs will become severe with the increase of the sample size. Although there exist many solvers for the SVMs, only few of them are designed by exploiting the special structures of the SVMs. In this paper, we propose a highly efficient sparse semismooth Newton based augmented Lagrangian method for solving a large-scale convex quadratic programming problem with a linear equality constraint and a simple box constraint, which is generated from the dual problems of the SVMs. By leveraging the primal-dual error bound result, the fast local convergence rate of the augmented Lagrangian method can be guaranteed. Furthermore, by exploiting the second-order sparsity of the problem when using the semismooth Newton method,the algorithm can efficiently solve the aforementioned difficult problems. Finally, numerical comparisons demonstrate that the proposed algorithm outperforms the current state-of-the-art solvers for the large-scale SVMs.