A nonmonotone line search technique for Newton's method
SIAM Journal on Numerical Analysis
Mathematical Programming: Series A and B
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
NE/SQP: a robust algorithm for the nonlinear complementarity problem
Mathematical Programming: Series A and B
Robust solution of mixed complementary problems
Robust solution of mixed complementary problems
Algorithms for complementarity problems and generalized equations
Algorithms for complementarity problems and generalized equations
Solution of monotone complementarity problems with locally Lipschitzian functions
Mathematical Programming: Series A and B - Special issue on computational nonsmooth optimization
QPCOMP: a quadratic programming based solver for mixed complementarity problems
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
Global methods for nonlinear complementarity problems
Mathematics of Operations Research
A New Class of Semismooth Newton-Type Methods for Nonlinear Complementarity Problems
Computational Optimization and Applications
Journal of Global Optimization
A family of NCP functions and a descent method for the nonlinear complementarity problem
Computational Optimization and Applications
Optimization Methods & Software
Hi-index | 7.29 |
Recently there have two different effective methods proposed by Kanzow et al. in (Kanzow, 2001 [8]) and (Kanzow and Petra, 2004 [9]), respectively, which commonly use the Fischer-Burmeister (FB) function to recast the mixed complementarity problem (MCP) as a constrained minimization problem and a nonlinear system of equations, respectively. They all remark that their algorithms may be improved if the FB function is replaced by other NCP functions. Accordingly, in this paper, we employ the generalized Fischer-Burmeister (GFB) where the 2-norm in the FB function is relaxed to a general p-norm (p1) for the two methods and investigate how much the improvement is by changing the parameter p as well as which method is influenced more when we do so, by the performance profiles of iterations and function evaluations for the two methods with different p on MCPLIB collection.