Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/318860 
Year of Publication: 
2024
Citation: 
[Journal:] Optimization Letters [ISSN:] 1862-4480 [Volume:] 19 [Issue:] 2 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2024 [Pages:] 267-284
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
We propose a gradient-based method to solve quasiconvex optimization problems through decomposed optimization and prove local superlinear convergence under mild regularity assumptions at the optimal solution. A practical implementation further provides global convergence while maintaining the fast local convergence. In numerical examples from generalized cone programming, the proposed method reduced the number of iterations to 18 to 50% compared to bisection.
Subjects: 
Quasiconvex optimization
Decomposed optimization
Newton’s method
Generalized cone problems
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.