An Adaptive Univariate Global Optimization Algorithm and Its Convergence Rate for Twice Continuously Differentiable Functions

Authors

James M. Calvin, Department of Computer Science
Yvonne Chen, Department of Computer Science
Antanas Žilinskas, Vilniaus Universitetas

Document Type

Article

Publication Date

11-1-2012

Abstract

We describe an adaptive algorithm for approximating the global minimum of a continuous univariate function. The convergence rate of the error is studied for the case of a twice continuously differentiable function. © 2012 Springer Science+Business Media, LLC.

Identifier

84869110921 (Scopus)

Publication Title

Journal of Optimization Theory and Applications

External Full Text Location

https://doi.org/10.1007/s10957-012-0060-3

e-ISSN

15732878

ISSN

00223239

First Page

628

Last Page

636

Issue

2

Volume

155

Grant

CMMI-0825381

Fund Ref

National Science Foundation

Recommended Citation

Calvin, James M.; Chen, Yvonne; and Žilinskas, Antanas, "An Adaptive Univariate Global Optimization Algorithm and Its Convergence Rate for Twice Continuously Differentiable Functions" (2012). Faculty Publications. 18045.
https://digitalcommons.njit.edu/fac_pubs/18045