A nonmonotone line search technique for Newton's method
SIAM Journal on Numerical Analysis
Mathematical Programming: Series A and B
On the resolution of monotone complementarity problems
Computational Optimization and Applications
Modified Newton methods for solving a semismooth reformulation of monotone complementarity problems
Mathematical Programming: Series A and B - Special issue on computational nonsmooth optimization
Solution of monotone complementarity problems with locally Lipschitzian functions
Mathematical Programming: Series A and B - Special issue on computational nonsmooth optimization
A Comparison of Large Scale Mixed Complementarity Problem Solvers
Computational Optimization and Applications
A New Class of Semismooth Newton-Type Methods for Nonlinear Complementarity Problems
Computational Optimization and Applications
Error bounds for R0-type and monotone nonlinear complementarity problems
Journal of Optimization Theory and Applications
A Linearly Convergent Derivative-Free Descent Method for Strongly Monotone Complementarity Problems
Computational Optimization and Applications
A family of NCP functions and a descent method for the nonlinear complementarity problem
Computational Optimization and Applications
Journal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics
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In this paper, we propose a new generalized penalized Fischer---Burmeister merit function, and show that the function possesses a system of favorite properties. Moreover, for the merit function, we establish the boundedness of level set under a weaker condition. We also propose a derivative-free algorithm for nonlinear complementarity problems with a nonmonotone line search. More specifically, we show that the proposed algorithm is globally convergent and has a locally linear convergence rate. Numerical comparisons are also made with the merit function used by Chen (J Comput Appl Math 232:455---471, 2009), which confirm the superior behaviour of the new merit function.