Minimization Algorithms Based on Supervisor and Searcher Cooperation

W. Liu and Y. H. Dai
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W. Liu: University of Kent
Y. H. Dai: Chinese Academy of Sciences

Journal of Optimization Theory and Applications, 2001, vol. 111, issue 2, No 7, 359-379

Abstract: 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.

Keywords: Robust algorithms; noisy optimization; gradient algorithms; stochastic approximations (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (3)

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DOI: 10.1023/A:1011986402461

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