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- W2119444311 abstract "Four algorithms, all variants of Simultaneous Perturbation Stochastic Approximation (SPSA), are proposed. The original one-measurement SPSA uses an estimate of the gradient of objective function L containing an additional bias term not seen in two-measurement SPSA. As a result, the asymptotic covariance matrix of the iterate convergence process has a bias term. We propose a one-measurement algorithm that eliminates this bias, and has asymptotic convergence properties making for easier comparison with the two-measurement SPSA. The algorithm, under certain conditions, outperforms both forms of SPSA with the only overhead being the storage of a single measurement. We also propose a similar algorithm that uses perturbations obtained from normalized Hadamard matrices. The convergence w.p. 1 of both algorithms is established. We extend measurement reuse to design two second-order SPSA algorithms and sketch the convergence analysis. Finally, we present simulation results on an illustrative minimization problem." @default.
- W2119444311 created "2016-06-24" @default.
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- W2119444311 date "2006-12-03" @default.
- W2119444311 modified "2023-09-24" @default.
- W2119444311 title "SPSA algorithms with measurement reuse" @default.
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- W2119444311 doi "https://doi.org/10.5555/1218112.1218174" @default.
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