A general class of algorithms for unconstrained optimization is introduced, which subsumes the classical trust-region algorithm and two of its newer variants, as well as the cubic and quadratic regularization methods. A unified theory of global convergence to first-order critical points is then described for this class. An extension to projection-based trust-region algorithms for nonlinear optimization over convex sets is also presented.
Citation
Report TR08-15, Department of Mathematics, FUNDP-University of Namur, Namur, Belgium
Article
View Nonlinear Stepsize Control, Trust Regions and Regularizations for Unconstrained Optimization