Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/60984
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Title: An unconstrained differentiable penalty method for implicit complementarity problems
Authors: Tian, B
Li, D
Yang, X 
Issue Date: 2016
Source: Optimization methods and software, 2016, v. 31, no. 4, p. 775-790
Abstract: In this paper, we introduce an unconstrained differentiable penalty method for solving implicit complementarity problems, which has an exponential convergence rate under the assumption of a uniform ξ-P-function. Instead of solving the unconstrained penalized equations directly, we consider a corresponding unconstrained optimization problem and apply the trust-region Gauss–Newton method to solve it. We prove that the local solution of the unconstrained optimization problem identifies that of the complementarity problems under monotone assumptions. We carry out numerical experiments on the test problems from MCPLIB, and show that the proposed method is efficient and robust.
Keywords: Exponential convergence rate
Implicit complementarity problems
Lower order penalty method
Trust-region Gauss–Newton method
Publisher: Taylor & Francis
Journal: Optimization methods and software 
ISSN: 1055-6788
DOI: 10.1080/10556788.2016.1146266
Rights: © 2016 Informa UK Limited, trading as Taylor & Francis Group
This is an Accepted Manuscript of an article published by Taylor & Francis in Optimization Methods and Software on 24 Feb 2016 (published online), available at: http://www.tandfonline.com/10.1080/10556788.2016.1146266
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