A box-constrained differentiable penalty method for nonlinear complementarity problems

Boshi Tian (), Yaohua Hu () and Xiaoqi Yang ()

Journal of Global Optimization, 2015, vol. 62, issue 4, 729-747

Abstract: In this paper, we propose a box-constrained differentiable penalty method for nonlinear complementarity problems, which not only inherits the same convergence rate as the existing $$\ell _\frac{1}{p}$$ ℓ 1 p -penalty method but also overcomes its disadvantage of non-Lipschitzianness. We introduce the concept of a uniform $$\xi $$ ξ – $$P$$ P -function with $$\xi \in (1,2]$$ ξ ∈ ( 1 , 2 ] , and apply it to prove that the solution of box-constrained penalized equations converges to that of the original problem at an exponential order. Instead of solving the box-constrained penalized equations directly, we solve a corresponding differentiable least squares problem by using a trust-region Gauss–Newton method. Furthermore, we establish the connection between the local solution of the least squares problem and that of the original problem under mild conditions. We carry out the numerical experiments on the test problems from MCPLIB, and show that the proposed method is efficient and robust. Copyright Springer Science+Business Media New York 2015

Keywords: Nonlinear complementarity problem; $$\ell _\frac{1}{p}$$ ℓ 1 p -penalty method; Differentiable penalty method; Convergence rate; Least squares method; 90C33; 65K15; 49M30 (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s10898-015-0275-6

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