A nonsmooth version of Newton's method
Mathematical Programming: Series A and B
Convergence analysis of some algorithms for solving nonsmooth equations
Mathematics of Operations Research
Inexact trust region method for large sparse systems of nonlinear equations
Journal of Optimization Theory and Applications
Growth behavior of a class of merit functions for the nonlinear complementarity problem
Journal of Optimization Theory and Applications
On the resolution of monotone complementarity problems
Computational Optimization and Applications
A semismooth equation approach to the solution of nonlinear complementarity problems
Mathematical Programming: Series A and B
A New Nonsmooth Equations Approach to Nonlinear Complementarity Problems
SIAM Journal on Control and Optimization
Optimization: algorithms and consistent approximations
Optimization: algorithms and consistent approximations
A New Class of Semismooth Newton-Type Methods for Nonlinear Complementarity Problems
Computational Optimization and Applications
On the local convergence of quasi-Newton methods for nonlinear complementarity problems
Selected papers of the second Panamerican workshop on Applied and computational mathematics
A Theoretical and Numerical Comparison of Some Semismooth Algorithms for Complementarity Problems
Computational Optimization and Applications
Direct search methods: then and now
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
A New Merit Function For Nonlinear Complementarity Problems And A Related Algorithm
SIAM Journal on Optimization
Newton and Quasi-Newton Methods for a Class of Nonsmooth Equations and Related Problems
SIAM Journal on Optimization
Hybrid Newton-type method for a class of semismooth equations
Journal of Optimization Theory and Applications
A new smoothing and regularization Newton method for P0-NCP
Journal of Global Optimization
A new smoothing Broyden-like method for solving nonlinear complementarity problem with a P0-function
Journal of Global Optimization
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It is known that a nonlinear complementarity problem (NCP) can be reformulated as a semismooth system of nonlinear equations by using a so-called NCP-function. Global Newton-type methods for solving NCP via semismooth reformulation need to use a merit function, which is usually required to be continuously differentiable. In this paper we present a global Newton-type method which does not require the differentiability for the merit function used in the line-search procedure. The method is used to numerically compare the effectiveness of two NCP-functions widely discussed in literature, the minimum function and the Fischer-Burmeister function. The results on several examples allow to gain some new acquaintance of the respective numerical advantages of the two functions.