Liu, W.B. and 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.
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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.
|Subjects:||H Social Sciences > HA Statistics > HA33 Management Science|
|Divisions:||Faculties > Social Sciences > Kent Business School|
|Depositing User:||Steve Wenbin Liu|
|Date Deposited:||03 Sep 2008 13:45|
|Last Modified:||14 Jan 2010 14:31|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/8490 (The current URI for this page, for reference purposes)|
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