The proximal gradient algorithm for minimizing the sum of a smooth and nonsmooth convex function often converges linearly even without strong convexity. One common reason is that …
This paper sheds new light on several interrelated topics of second-order variational analysis, both in finite and infinite-dimensional settings. We establish new relationships …
This paper proposes and justifies two globally convergent Newton-type methods to solve unconstrained and constrained problems of nonsmooth optimization by using tools of …
The paper is devoted to the study, characterizations, and applications of variational convexity of functions, the property that has been recently introduced by Rockafellar together …
H Gfrerer, BS Mordukhovich - SIAM Journal on Optimization, 2015 - SIAM
This paper is devoted to the study of tilt stability of local minimizers for classical nonlinear programs with equality and inequality constraints in finite dimensions described by twice …
This paper proposes and develops a new Newton-type algorithm to solve subdifferential inclusions defined by subgradients of extended real-valued prox-regular functions. The …
H Gfrerer, JV Outrata - Journal of Mathematical Analysis and Applications, 2022 - Elsevier
The paper deals with a comprehensive theory of mappings, whose local behavior can be described by means of linear subspaces, contained in the graphs of two (primal and dual) …
The paper concerns a systematic study of full stability in general optimization models including its conventional Lipschitzian version as well as the new Hölderian one. We …
The paper is devoted to full stability of optimal solutions in general settings of finite- dimensional optimization with applications to particular models of constrained optimization …