On the convergence of the proximal point algorithm for convex minimization
SIAM Journal on Control and Optimization
Proximal minimization algorithm with D-functions
Journal of Optimization Theory and Applications
Nonlinear proximal point algorithms using Bregman functions, with applications to convex programming
Mathematics of Operations Research
Mathematical Programming: Series A and B
Mathematical Programming: Series A and B
A Generalized Proximal Point Algorithm for the Variational Inequality Problem in a Hilbert Space
SIAM Journal on Optimization
Proximal Point Methods and Nonconvex Optimization
Journal of Global Optimization
Self-adaptive inexact proximal point methods
Computational Optimization and Applications
Pseudomonotone operators and the Bregman Proximal Point Algorithm
Journal of Global Optimization
Convergence analysis of an extended auxiliary problem principle with various stopping criteria
Optimization Methods & Software
| Hi-index | 0.00 |
A generalized proximal point algorithm for the minimization of a nonconvex function on a feasible set is investigated. It is known that if the objective function of the given problem is (lower semicontinuous, proper and) convex, well-definedness of the method as well as convergence of the generated iterates, being the solutions of better conditioned and uniquely solvable subproblems, are known. The present paper contributes to the discussion of the methods' behaviour when the objective is not convex. This gives rise to questions, among others, of well-definedness and convergence of the generated sequence.