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DERIVATIVE-FREE OPTIMIZATION OF NOISY FUNCTIONS VIA QUASI-NEWTON METHODS.
- Source :
-
SIAM Journal on Optimization . 2019, Vol. 29 Issue 2, p965-993. 29p. - Publication Year :
- 2019
-
Abstract
- This paper presents a finite-difference quasi-Newton method for the minimization of noisy functions. The method takes advantage of the scalability and power of BFGS updating, and employs an adaptive procedure for choosing the differencing interval h based on the noise estimation techniques of Hamming [Introduction to Applied Numerical Analysis, Courier Corporation, North Chelmsford, MA, 2012] and Mor\'e and Wild [SIAM J. Sci. Comput., 33 (2011), pp. 1292--1314]. This noise estimation procedure and the selection of h are inexpensive but not always accurate, and to prevent failures the algorithm incorporates a recovery mechanism that takes appropriate action in the case when the line-search procedure is unable to produce an acceptable point. A novel convergence analysis is presented that considers the effect of a noisy line-search procedure. Numerical experiments comparing the method to a function interpolating trust-region method are presented. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10526234
- Volume :
- 29
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- SIAM Journal on Optimization
- Publication Type :
- Academic Journal
- Accession number :
- 139017648
- Full Text :
- https://doi.org/10.1137/18M1177718