Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103355
Title: A Superlinearly Convergent Smoothing Newton Continuation Algorithm for Variational Inequalities over Definable Sets
Authors: Chua, Chek Beng
Hien, L. T. K.
Issue Date: 2015
Source: Chua, C. B., & Hien, L. T. K. (2015). A Superlinearly Convergent Smoothing Newton Continuation Algorithm for Variational Inequalities over Definable Sets. SIAM Journal on Optimization, 25(2), 1034-1063.
Series/Report no.: SIAM Journal on Optimization
Abstract: In this paper, we use the concept of barrier-based smoothing approximations introduced by Chua and Li [SIAM J. Optim., 23 (2013), pp. 745--769] to extend the smoothing Newton continuation algorithm of Hayashi, Yamashita, and Fukushima [SIAM J. Optim., 15 (2005), pp. 593--615] to variational inequalities over general closed convex sets X. We prove that when the underlying barrier has a gradient map that is definable in some o-minimal structure, the iterates generated converge superlinearly to a solution of the variational inequality. We further prove that if X is proper and definable in the o-minimal structure Ran, then the gradient map of its universal barrier is definable in the o-minimal expansion Ran,exp. Finally, we consider the application of the algorithm to complementarity problems over epigraphs of matrix operator norm and nuclear norm and present preliminary numerical results.
URI: https://hdl.handle.net/10356/103355
http://hdl.handle.net/10220/38744
DOI: 10.1137/140957615
Rights: © 2015 Society for Industrial and Applied Mathematics (SIAM). This paper was published in SIAM Journal on Optimization and is made available as an electronic reprint (preprint) with permission of Society for Industrial and Applied Mathematics (SIAM). The published version is available at: [http://dx.doi.org/10.1137/140957615]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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