TITLE:
A Regularized Newton Method with Correction for Unconstrained Convex Optimization
AUTHORS:
Liming Li, Mei Qin, Heng Wang
KEYWORDS:
Regularied Newton Method, Correction Technique, Trust Region Technique, Unconstrained Convex Optimization
JOURNAL NAME:
Open Journal of Optimization,
Vol.5 No.1,
March
15,
2016
ABSTRACT: In this
paper, we present a regularized Newton method (M-RNM) with correction for
minimizing a convex function whose Hessian matrices may be singular. At every
iteration, not only a RNM step is computed but also two correction steps are
computed. We show that if the objective function is LC2, then the method posses globally convergent.
Numerical results show that the new algorithm performs very well.