A Trust Region Method for Norm Constrained Problems
Abstract
In this paper a trust region method for the
solution of nonlinear optimization problems with norm constraints
is presented and analyzed.
Characterizations and the descent properties of
trust region steps are given,
criteria for the existence of successful iterations under
inexact gradient information and under the use of subspace methods
are established, and global convergence of the method is proven.
Keywords
Trust region methods, global convergence, norm constraints,
nonlinear least squares, Gauss--Newton method.
AMS subject classifications
90C30, 65K05