Abstract
The traditional BFGS algorithm has been proved very efficient. It is convergent for convex nonlinear optimization problems. However, for non-convex nonlinear optimization problems, it is known that the BFGS algorithm may not be convergent. This paper proposes a robust BFGS algorithm in the sense that the algorithm superlinearly converges to a local minimum under some mild assumptions for both convex and non-convex nonlinear optimization problems. Numerical test on the CUTEst test set is reported to demonstrate the merit of the proposed robust BFGS algorithm. This result shows that the robust BFGS algorithm is very efficient and effective.
Sign-in/Register to access full text options 
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.
