Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/83347
Title: A finite Newton algorithm for non-degenerate piecewise linear systems
Authors: Yuan, X.-T. 
Yan, S. 
Issue Date: 2011
Source: 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
URI: http://scholarbank.nus.edu.sg/handle/10635/83347
ISSN: 15324435
Appears in Collections:Staff Publications

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