Abstract
The trust region method plays an important role in solving optimization problems. In this paper, we propose a new nonmonotone adaptive trust region method for solving unconstrained optimization problems. Actually, we combine a popular nonmonotone technique with an adaptive trust region algorithm. The new ratio to adjusting the next trust region radius is different from the ratio in the traditional trust region methods. Under some appropriate conditions, we show that the new algorithm has good global convergence and superlinear convergence.
Highlights
A New Nonmonotone Adaptive Trust Region MethodHow to cite this paper: Zhang, Y., Ji, Q.M. and Zhou, Q.H. (2021) A New Nonmonotone Adaptive Trust Region Method
In this paper, we consider the following unconstrained optimization problem: min f ( x), x∈Rn (1.1)where f ( x) : Rn → R is a real-valued twice continuously differentiable function
We propose a new nonmonotone adaptive trust region method for solving unconstrained optimization problems
Summary
How to cite this paper: Zhang, Y., Ji, Q.M. and Zhou, Q.H. (2021) A New Nonmonotone Adaptive Trust Region Method. How to cite this paper: Zhang, Y., Ji, Q.M. and Zhou, Q.H. (2021) A New Nonmonotone Adaptive Trust Region Method. Journal of Applied Mathematics and Physics, 9, 3102-3114. Received: October 30, 2021 Accepted: December 20, 2021 Published: December 23, 2021
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
