Optimization Online


A proximal-Newton method for unconstrained convex optimization in Hilbert spaces

Maicon Marques Alves(maicon.alves***at***ufsc.br)
Benar Fux Svaiter(benar***at***impa.br)

Abstract: We propose and study the iteration-complexity of a proximal-Newton method for finding approximate solutions of the problem of minimizing a twice continuously differentiable convex function on a (possibly infinite dimensional) Hilbert space. We prove global convergence rates for obtaining approximate solutions in terms of function/gradient values. Our main results follow from an iteration-complexity study of an (large-step) inexact proximal point method for solving convex minimization problems.

Keywords: Smooth convex optimization, Proximal-Newton method, Complexity, Proximal point methods

Category 1: Convex and Nonsmooth Optimization


Download: [PDF]

Entry Submitted: 10/29/2016
Entry Accepted: 10/29/2016
Entry Last Modified: 10/29/2016

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society