Existence and Limiting Behavior of Trajectories Associatedwith P0-equations
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Journal of Optimization Theory and Applications
Complexity of a noninterior path-following method for the linear complementarity problem
Journal of Optimization Theory and Applications
A Smoothing Newton Method for General Nonlinear Complementarity Problems
Computational Optimization and Applications
Computational Optimization and Applications
Quadratic one-step smoothing Newton method for P0-LCP without strict complementarity
Applied Mathematics and Computation
Applications of smoothing methods in numerical analysis and optimization
Focus on computational neurobiology
A New Path-Following Algorithm for Nonlinear P*Complementarity Problems
Computational Optimization and Applications
Smoothing-type algorithm for solving linear programs by using an augmented complementarity problem
Applied Mathematics and Computation
A smoothing-type algorithm for solving system of inequalities
Journal of Computational and Applied Mathematics
A variant smoothing Newton method for P0-NCP based on a new smoothing function
Journal of Computational and Applied Mathematics
A non-interior continuation algorithm for the CP based on a generalized smoothing function
Journal of Computational and Applied Mathematics
A Continuation Method for Nonlinear Complementarity Problems over Symmetric Cones
SIAM Journal on Optimization
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We propose a continuation method for a class of nonlinear complementarity problems (NCPs), including the NCP with a P0 and R0 function and the monotone NCP with a feasible interior point. The continuation method is based on a class of Chen--Mangasarian smoothing functions. Unlike many existing continuation methods, the method follows noninterior smoothing paths, and, as a result, initial points can be easily constructed. In addition, we introduce a procedure to dynamically update the neighborhoods associated with the smoothing paths, so that the algorithm is both globally convergent and locally superlinearly convergent under suitable assumptions. Finally, a hybrid continuation-smoothing method is proposed and is shown to have the same convergence properties under weaker conditions.