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
A non-interior-point continuation method for linear complementarity problems
SIAM Journal on Matrix Analysis and Applications
A Globally Convergent Successive Approximation Method for Severely Nonsmooth Equations
SIAM Journal on Control and Optimization
A class of smoothing functions for nonlinear and mixed complementarity problems
Computational Optimization and Applications
Some Noninterior Continuation Methods for LinearComplementarity Problems
SIAM Journal on Matrix Analysis and Applications
A semismooth equation approach to the solution of nonlinear complementarity problems
Mathematical Programming: Series A and B
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
Superlinear convergence of smoothing quasi-Newton methods for nonsmooth equations
Journal of Computational and Applied Mathematics
Equivalent Unconstrained Minimization and Global Error Bounds for Variational Inequality Problems
SIAM Journal on Control and Optimization
New NCP-functions and their properties
Journal of Optimization Theory and Applications
A continuation method for (strongly) monotone variational inequalities
Mathematical Programming: Series A and B
Mathematical Programming: Series A and B
A Theoretical and Numerical Comparison of Some Semismooth Algorithms for Complementarity Problems
Computational Optimization and Applications
Computational Optimization and Applications - Special issue on nonsmooth and smoothing methods
SIAM Journal on Optimization
Smooth Approximations to Nonlinear Complementarity Problems
SIAM Journal on Optimization
Applications of smoothing methods in numerical analysis and optimization
Focus on computational neurobiology
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The mixed complementarity problem can be reformulated as a nonsmooth equation by using the median operator. In this paper, we first study some useful properties of this reformulation and then derive the Chen-Harker-Kanzow-Smale smoothing function for the mixed complementarity problem. On the basis of this smoothing function, we present a smoothing Newton method for solving the mixed complementarity problem. Under suitable conditions, the method exhibits global and quadratic convergence properties. We also present a smoothing Broyden-like method based on the same smoothing function. Under appropriate conditions, the method converges globally and superlinearly.