More test examples for nonlinear programming codes
More test examples for nonlinear programming codes
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
Superlinearly convergent approximate Newton methods for LC1 optimization problems
Mathematical Programming: Series A and B
A Globally Convergent Successive Approximation Method for Severely Nonsmooth Equations
SIAM Journal on Control and Optimization
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
Quasi-inexact-Newton methods with global convergence for solving constrained nonlinear systems
Nonlinear Analysis: Theory, Methods & Applications
Perturbation lemma for the Newton method with application to the SQP Newton method
Journal of Optimization Theory and Applications
Inexact perturbed Newton methods and applications to a class of Krylov solvers
Journal of Optimization Theory and Applications
Globally convergent inexact generalized Newton's methods for nonsmooth equations
Journal of Computational and Applied Mathematics
SIAM Journal on Optimization
A nonmonotone semismooth inexact Newton method
Optimization Methods & Software
Inexact quasi-Newton global convergent method for solving constrained nonsmooth equations
International Journal of Computer Mathematics
Journal of Computational and Applied Mathematics
Some superlinearly convergent inexact generalized Newton method for solving nonsmooth equations
Optimization Methods & Software
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In this paper, we present the combination of the inexact Newton method and the generalized Newton method for solving nonsmooth equations F(x)驴=驴0, characterizing the local convergence in terms of the perturbations and residuals. We assume that both iteration matrices taken from the B-differential and vectors F(x (k)) are perturbed at each step. Some results are motivated by the approach of C驴tina驴 regarding to smooth equations. We study the conditions, which determine admissible magnitude of perturbations to preserve the convergence of method. Finally, the utility of these results is considered based on some variant of the perturbed inexact generalized Newton method for solving some general optimization problems.