Abstract
Multidimensional stochastic optimization plays an important role in the analysis and control of many technical systems. Randomized algorithms of stochastic approximation with perturbed input have been suggested for solving the challenging multidimensional problems of optimization. These algorithms have simple forms and provide consistent estimates of the unknown parameters for observations under almost arbitrary noise. They are easily incorporated into the design of quantum devices for estimating the gradient vector of a multivariable function.
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© 2015 Springer-Verlag Berlin Heidelberg
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Granichin, O., Volkovich, Z.(., Toledano-Kitai, D. (2015). Randomized Stochastic Approximation. In: Randomized Algorithms in Automatic Control and Data Mining. Intelligent Systems Reference Library, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54786-7_3
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DOI: https://doi.org/10.1007/978-3-642-54786-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54785-0
Online ISBN: 978-3-642-54786-7
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