Abstract
This paper gives a variant trust-region method, where its radius is automatically adjusted by using the model information gathered at the current and preceding iterations. The primary aim is to decrease the number of function evaluations and solving subproblems, which increases the efficiency of the trust-region method. The next aim is to update the new radius for large-scale problems without imposing too much computational cost to the scheme. Global convergence to first-order stationary points is proved under classical assumptions. Preliminary numerical experiments on a set of test problems from the CUTEst collection show that the presented method is promising for solving unconstrained optimization problems.
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
