AN ADAPTIVE GRADIENT ALGORITHM FOR LARGE-SCALE NONLINEAR BOUND CONSTRAINED OPTIMIZATION

摘要

In this paper, an adaptive gradient algorithm (AGM) for box constrained optimization is developed. The algorithm is based on an active set identification technique and consists of a nonmonotone gradient projection step, a conjugate gradient step and a rule for branching between the two steps. We show that the method is globally convergent under appropriate conditions. Numerical experiments are presented using bound constrained problems in the CUTEr test problem library.