A derivative-free filter method for solving nonlinear complementarity problems

2005 ◽  
Vol 161 (3) ◽  
pp. 787-797 ◽  
Author(s):  
Pu-yan Nie ◽  
Jin-yan Fan
Keyword(s):  
Complementarity Problems ◽  
Nonlinear Complementarity Problems ◽  
Filter Method ◽  
Nonlinear Complementarity ◽  
Derivative Free

scholarly journals Penalized NCP-functions for nonlinear complementarity problems and a scaling algorithm

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Jueyu Wang ◽  
Chao Gu ◽  
Guoqiang Wang
Keyword(s):  
Complementarity Problems ◽  
Numerical Results ◽  
Nonlinear Complementarity Problems ◽  
Stationary Points ◽  
Nonlinear Complementarity ◽  
Derivative Free

<p style='text-indent:20px;'>In this paper, we systematically study the properties of penalized NCP-functions in derivative-free algorithms for nonlinear complementarity problems (NCPs), and give some regular conditions for stationary points of penalized NCP-functions to be solutions of NCPs. The main contribution is to unify and generalize previous results. Based on one of above penalized NCP-functions, we analyze a scaling algorithm for NCPs. The numerical results show that the scaling can greatly improve the effectiveness of the algorithm.</p>

scholarly journals Convergence Analysis of a Trust-Region Multidimensional Filter Method for Nonlinear Complementarity Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
C. W. Wu ◽  
J. P. Cao ◽  
L. F. Wang
Keyword(s):  
Quadratic Programming ◽  
Convergence Analysis ◽  
Numerical Experiments ◽  
Complementarity Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Nonlinear Complementarity Problems ◽  
Filter Method ◽  
Nonlinear Complementarity ◽  
Globally Convergent

For solving nonlinear complementarity problems, a new algorithm is proposed by using multidimensional filter techniques and a trust-region method. The algorithm is shown to be globally convergent under the reasonable assumptions and does not depend on any extra restoration procedure. In particular, it shows that the subproblem is a convex quadratic programming problem, which is easier to be solved. The results of numerical experiments show its efficiency.

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