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 globally convergent Newton method for convex SC1minimization problems
Journal of Optimization Theory and Applications
Inexact Newton methods for solving nonsmooth equations
Proceedings of the international meeting on Linear/nonlinear iterative methods and verification of solution
Inexact generalized Newton methods for second order C-differentiable optimization
Journal of Computational and Applied Mathematics
Inexact quasi-Newton global convergent method for solving constrained nonsmooth equations
International Journal of Computer Mathematics
Reference variable methods of solving min---max optimization problems
Journal of Global Optimization
Globally convergent Jacobian smoothing inexact Newton methods for NCP
Computational Optimization and Applications
Journal of Computational and Applied Mathematics
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In this paper, motivated by the Martinez and Qi methods (J. Comput. Appl. Math. 60 (1995) 127), we propose one type of globally convergent inexact generalized Newton's methods to solve nonsmooth equations in which the functions are nondifferentiable, but are Lipschitz continuous. The methods make the norm of the functions decreasing. These methods are implementable and globally convergent. We also prove that the algorithms have superlinear convergence rates under some mild conditions.