Skip to main content

Minimization Algorithms Based On Supervisor and Searcher Co-operation

Liu, Wenbin, Dai, Y.H. (2001) Minimization Algorithms Based On Supervisor and Searcher Co-operation. Journal of Optimization Theory and Applications, 111 (2). pp. 359-379. ISSN 0022-3239. (doi:10.1023/A:1011986402461) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:8490)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1023/A:1011986402461

Abstract

In the present work, we explore a general framework for the design of new minimization algorithms with desirable characteristics, namely, supervisor-searcher cooperation. We propose a class of algorithms within this framework and examine a gradient algorithm in the class. Global convergence is established for the deterministic case in the absence of noise and the convergence rate is studied. Both theoretical analysis and numerical tests show that the algorithm is efficient for the deterministic case. Furthermore, the fact that there is no line search procedure incorporated in the algorithm seems to strengthen its robustness so that it tackles effectively test problems with stronger stochastic noises. The numerical results for both deterministic and stochastic test problems illustrate the appealing attributes of the algorithm.

Item Type: Article
DOI/Identification number: 10.1023/A:1011986402461
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Steve Liu
Date Deposited: 03 Sep 2008 13:45 UTC
Last Modified: 16 Feb 2021 12:19 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/8490 (The current URI for this page, for reference purposes)
Liu, Wenbin: https://orcid.org/0000-0001-5966-6235
  • Depositors only (login required):