Please use this identifier to cite or link to this item:
Title: A finite Newton algorithm for non-degenerate piecewise linear systems
Authors: Yuan, X.-T. 
Yan, S. 
Issue Date: 2011
Citation: Yuan, X.-T.,Yan, S. (2011). A finite Newton algorithm for non-degenerate piecewise linear systems. Journal of Machine Learning Research 15 : 841-854. ScholarBank@NUS Repository.
Abstract: We investigate Newton-type optimization methods for solving piecewise linear systems (PLS) with non-degenerate 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 paper, we propose an effective damped Newton method, namely PLSDN, to find the exact solution of non-degenerate PLS. PLS-DN exhibits provable semi-iterative property, i.e., 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 to modeling, from a novel perspective of PLS, several statistical learning problems such as elitist Lasso, non-negative least squares and support vector machines. Numerical results on synthetic and benchmark data sets are presented to demonstrate the effectiveness and efficiency of PLS-DN on these problems. Copyright 2011 by the authors.
Source Title: Journal of Machine Learning Research
ISSN: 15324435
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Jun 21, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.