A non-interior-point continuation method for linear complementarity problems
SIAM Journal on Matrix Analysis and Applications
Machine Learning
Some Noninterior Continuation Methods for LinearComplementarity Problems
SIAM Journal on Matrix Analysis and Applications
Weak Univalence and Connectedness of Inverse Images of Continuous Functions
Mathematics of Operations Research
Existence and Limiting Behavior of Trajectories Associatedwith P0-equations
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Improving the convergence of non-interior point algorithms for nonlinear complementarity problems
Mathematics of Computation
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
A Smoothing Newton Method for Minimizing a Sum of Euclidean Norms
SIAM Journal on Optimization
Regularization of P0-Functions in Box Variational Inequality Problems
SIAM Journal on Optimization
SIAM Journal on Optimization
Interior-Point Methods for Massive Support Vector Machines
SIAM Journal on Optimization
A Nonmonotone Line Search Technique and Its Application to Unconstrained Optimization
SIAM Journal on Optimization
Sub-quadratic convergence of a smoothing Newton algorithm for the P0– and monotone LCP
Mathematical Programming: Series A and B
Computational Optimization and Applications
A smoothing-type algorithm for solving system of inequalities
Journal of Computational and Applied Mathematics
Smoothing algorithms for complementarity problems over symmetric cones
Computational Optimization and Applications
SIAM Journal on Optimization
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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.