A nonsmooth inexact Newton method for the solution of large-scale nonlinear 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
On the Accurate Identification of Active Constraints
SIAM Journal on Optimization
An Active Set Newton Algorithm for Large-Scale Nonlinear Programs with Box Constraints
SIAM Journal on Optimization
An affine scaling trust-region approach to bound-constrained nonlinear systems
Applied Numerical Mathematics
Projected Barzilai-Borwein methods for large-scale box-constrained quadratic programming
Numerische Mathematik
Expected Residual Minimization Method for Stochastic Linear Complementarity Problems
Mathematics of Operations Research
An interior-point affine-scaling trust-region method for semismooth equations with box constraints
Computational Optimization and Applications
Stochastic $R_0$ Matrix Linear Complementarity Problems
SIAM Journal on Optimization
Robust solution of monotone stochastic linear complementarity problems
Mathematical Programming: Series A and B
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Equations with box constraints are applied in many fields, for example the complementarity problem. After studying the existing methods, we find that quadratic convergence of majority algorithms is based on the solvability of the equations. But whether the equations are solvable is previously unknown. So, it is necessary to design an algorithm which has fast quadratic convergence. The quadratic convergence does not depend on the solvability of the equations. In this paper, we propose a new method for solving equations. The global and local quadratic convergence of the proposed algorithm are established under some suitable assumptions. We apply the proposed algorithm to a class of stochastic linear complementarity problems. Numerical results show that our method is valid.