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Convergence of the Kiefer-Wolfowitz algorithm in the presence of discontinuities

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  • Miklos Rasonyi
  • Kinga Tikosi

Abstract

In this paper we estimate the expected error of a stochastic approximation algorithm where the maximum of a function is found using finite differences of a stochastic representation of that function. An error estimate of $O(n^{-1/5})$ for the $n$th iteration is achieved using suitable parameters. The novelty with respect to previous studies is that we allow the stochastic representation to be discontinuous and to consist of possibly dependent random variables (satisfying a mixing condition).

Suggested Citation

  • Miklos Rasonyi & Kinga Tikosi, 2020. "Convergence of the Kiefer-Wolfowitz algorithm in the presence of discontinuities," Papers 2007.14069, arXiv.org, revised Sep 2021.
    • Handle: RePEc:arx:papers:2007.14069
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    File URL: http://arxiv.org/pdf/2007.14069
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    1. Laruelle Sophie & Pagès Gilles, 2012. "Stochastic approximation with averaging innovation applied to Finance," Monte Carlo Methods and Applications, De Gruyter, vol. 18(1), pages 1-51, January.
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