A new alternating direction trust region method based on conic model for solving unconstrained optimization

In this paper, a new alternating direction trust region method based on conic model is used to solve unconstrained optimization problems. By use of the alternating direction search method, the new conic model trust region subproblem is solved by two steps in two orthogonal directions. This new idea overcomes the shortcomings of conic model subproblem which is difficult to solve. Then the global convergence of the method under some reasonable conditions is established. Numerical experiment shows that this method may be better than the dogleg method to solve the subproblem, especially for large-scale problems.

Automated summary

In this paper, a new alternating direction trust region method based on conic model is proposed, solving the trust region subproblem in two orthogonal directions in two steps and overcoming the difficulties of solving the conic model subproblem. It shows better performance compared to the dogleg method, especially for large-scale problems.