Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
A filled function method for finding a global minimizer of a function of several variables
Mathematical Programming: Series A and B
Convergence analysis of some algorithms for solving nonsmooth equations
Mathematics of Operations Research
Nonlinear complementarity as unconstrained and constrained minimization
Mathematical Programming: Series A and B - Special issue: Festschrift in Honor of Philip Wolfe part II: studies in nonlinear programming
On stationary points of the implicit Lagrangian for nonlinear complementarity problems
Journal of Optimization Theory and Applications
On the resolution of monotone complementarity problems
Computational Optimization and Applications
Algorithms for complementarity problems and generalized equations
Algorithms for complementarity problems and generalized equations
A semismooth equation approach to the solution of nonlinear complementarity problems
Mathematical Programming: Series A and B
QPCOMP: a quadratic programming based solver for mixed complementarity problems
Mathematical Programming: Series A and B - Special issue on computational nonsmooth optimization
Implementation of a continuation method for normal maps
Mathematical Programming: Series A and B - Special issue on computational nonsmooth optimization
Interfaces to PATH 3.0: Design, Implementation and Usage
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
A New Merit Function For Nonlinear Complementarity Problems And A Related Algorithm
SIAM Journal on Optimization
The Lagrangian Globalization Method for Nonsmooth Constrained Equations
Computational Optimization and Applications
A Novel Filled Function Method and Quasi-Filled Function Method for Global Optimization
Computational Optimization and Applications
A new filled function method for an unconstrained nonlinear equation
Journal of Computational and Applied Mathematics
Global Descent Method for Global Optimization
SIAM Journal on Optimization
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We investigate the theoretical and numerical properties of two global optimization techniques for the solution of mixed complementarity problems. More precisely, using a standard semismooth Newton-type method as a basic solver for complementarity problems, we describe how the performance of this method can be improved by incorporating two well-known global optimization algorithms, namely a tunneling and a filled function method. These methods are tested and compared with each other on a couple of very difficult test examples.