Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/60984
Title: An unconstrained differentiable penalty method for implicit complementarity problems
Authors: Tian, B
Li, D
Yang, X 
Keywords: Exponential convergence rate
Implicit complementarity problems
Lower order penalty method
Trust-region Gauss–Newton method
Issue Date: 2016
Publisher: Taylor & Francis
Source: Optimization methods and software, 2016, v. 31, no. 4, p. 775-790 How to cite?
Journal: Optimization methods and software 
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.
URI: http://hdl.handle.net/10397/60984
ISSN: 1055-6788
DOI: 10.1080/10556788.2016.1146266
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