title: Nondegenerate piecewise linear systems: a finite Newton algorithm and applications in machine learning.
abstract: We investigate Newton-type optimization methods for solving piecewise linear systems (PLSs) with nondegenerate coefficient matrix. Such systems arise, for example, from the numerical solution of linear complementarity problem, which is useful to model several learning and optimization problems. In this letter, we propose an effective damped Newton method, PLS-DN, to find the exact (up to machine precision) solution of nondegenerate PLSs. PLS-DN exhibits provable semiiterative property, that is, the algorithm converges globally to the exact solution in a finite number of iterations. The rate of convergence is shown to be at least linear before termination. We emphasize the applications of our method in modeling, from a novel perspective of PLSs, some statistical learning problems such as box-constrained least squares, elitist Lasso (Kowalski & Torreesani, 2008), and support vector machines (Cortes & Vapnik, 1995). Numerical results on synthetic and benchmark data sets are presented to demonstrate the effectiveness and efficiency of PLS-DN on these problems.
authors.affiliation: Department of Statistics, Rutgers University, NJ 08854, USA. xyuan@stat.rutgers.edu
authors.forename: Xiao-Tong
authors.initials: XT
authors.lastname: Yuan
authors.forename: Shuicheng
authors.initials: S
authors.lastname: Yan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007858:Learning / Q000502:physiology; D016018:Least-Squares Analysis; D016014:Linear Models; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D060388:Support Vector Machine