An inexact smoothing-type algorithm for support vector machines

作者：Ni, Tie; Gu, Wei-Zhe^*; Zhai, Jun

来源：Neurocomputing, 2014, 129: 127-135.

DOI：10.1016/j.neucom.2013.09.047

• Support vector machines
• Non-monotone line search
• Smoothing-type algorithm
• Global convergence
• Superlinear convergence

The smoothing-type algorithm has been successfully applied to solve various optimization problems. In this paper, we propose an inexact smoothing-type algorithm for solving the generalized support vector machines based on a new class of smoothing functions. In general, the smoothing-type method is designed based on some monotone line search and solving a linear system of equations exactly at each iteration. However, for the large-scale problems, solving the linear system of equations exactly can be very expensive. In order to overcome these drawbacks, solving the linear system of equations inexactly and the non-monotone line search technique are used in our smoothing-type method. We show that the proposed algorithm is globally and locally superlinearly convergent under suitable assumptions. Preliminary numerical results are also reported.

• 出版日期2014-4-10
• 单位辽宁工程技术大学; 天津大学