Stopping Rules for Optimization Algorithms Based on Stochastic Approximation

Takayuki Wada () and Yasumasa Fujisaki ()
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Takayuki Wada: Osaka University
Yasumasa Fujisaki: Osaka University

Journal of Optimization Theory and Applications, 2016, vol. 169, issue 2, No 11, 568-586

Abstract: Abstract Stopping rules are developed for stochastic optimization algorithms, which minimize an unknown objective function using noise corrupted measurements. In particular, the finite-difference stochastic approximation and the simultaneous perturbation stochastic approximation are considered. The candidate solution after an adequate number of iterations is shown to be sufficiently close to the optimal solution in a mean squared sense. These numbers are determined by a priori information only. Furthermore, it is shown that these are polynomial order of the problem size.

Keywords: Stochastic approximation; Stochastic optimization; Stopping rule; 62L20; 90C99 (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10957-015-0808-7

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