Abstract
A new modified moving asymptotes method is presented. In each step of the iterative process, a strictly convex approximating subproblem is generated and explicitly solved. In doing so we propose a strategy to incorporate a modified second-order information for the moving asymptotes location. Under natural assumptions, we prove the geometrical convergence. In addition the experimental results reveal that the present method is significantly faster compared to the [1] method, Newton's method and the BFGS Method.
Highlights
In order to apply this method, it is necessary that the objective function should fulfill for each iteration k the following condition: (9)
Its performance degrades when it applied to nonconvex functions
It is the intention of this contribution to extend the [1] method to a more general context, by removing the restrictive condition (9) on the objective functions making the present approach more efficient, more general, and more competitive
Summary
Dedicated to Academician Professor Gradimir Milovanovic on the occasion of his 70th birthday. A new modified moving asymptotes method is presented. In each step of the iterative process, a strictly convex approximating subproblem is generated and explicitly solved. In doing so we propose a strategy to incorporate a modified second-order information for the moving asymptotes location. In addition the experimental results reveal that the present method is significantly faster compared to the [1] method, Newton’s method and the BFGS Method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
