Optimization Online


Cubic-regularization counterpart of a variable-norm trust-region method for unconstrained minimization

J. M. Martínez(martinez***at***ime.unicamp.br)
M. Raydan(mraydan***at***usb.ve)

Abstract: In a recent paper we introduced a trust-region method with variable norms for unconstrained minimization and we proved standard asymptotic convergence results. Here we will show that, with a simple modification with respect to the sufficient descent condition and replacing the trust-region approach with a suitable cubic regularization, the complexity of this method for finding approximate first-order stationary points is $O(\varepsilon^{-3/2})$. Some numerical experiments are also presented to illustrate the impact of the modification on practical performance.

Keywords: Smooth unconstrained minimization, cubic modeling, regularization, Newton-type methods.

Category 1: Nonlinear Optimization (Unconstrained Optimization )


Download: [PDF]

Entry Submitted: 11/17/2015
Entry Accepted: 11/17/2015
Entry Last Modified: 11/17/2015

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