- Cubic-regularization counterpart of a variable-norm trust-region method for unconstrained minimization J. M. Martínez(martinezime.unicamp.br) M. Raydan(mraydanusb.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 ) Citation: Download: [PDF]Entry Submitted: 11/17/2015Entry Accepted: 11/17/2015Entry Last Modified: 11/17/2015Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.