An active set truncated Newton method for large-scale bound constrained optimization

An active set truncated Newton method for large-scale bound constrained optimization is proposed. The active sets are guessed by an identification technique. The search direction consists of two parts: some of the components are simply defined; the other components are determined by the truncated Newton method. The method based on a nonmonotone line search technique is shown to be globally convergent. Numerical experiments are presented using bound constrained problems in the CUTEr test problem library. The numerical performance reveals that our method is effective and competitive with the famous algorithm TRON.