Recent Developments in Algorithms and Software for Trust-Region Methods

摘要

Trust-region methods are an important class of iterative methods for the solution of nonlinear optimization problems. Algorithms in this class have been proposed for the solution of systems of nonlinear equations, nonlinear estimation problems, unconstrained and constrained optimization, nondifferentiable optimization, and large-scale optimization. Interest in trust-region methods derives, in part, from the availability of strong-convergence results and from the development of software for these methods which is reliable, efficient, and amazingly free of ad-hoc decisions. In this paper we survey the theoretical and practical results available for trust-region methods and discuss the relevance of these results to the implementation of trust-region methods. (ERA citation 08:019359)