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Random Directions Stochastic Approximation With Deterministic Perturbations
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DOI：10.1109/TAC.2019.2930821.png)
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摘要
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We introduce deterministic perturbation (DP) schemes for the recently proposed random directions stochastic approximation, and propose new first-order and second-order algorithms. In the latter case, these are the first second-order algorithms to incorporate DPs. We show that the gradient and/or Hessian estimates in the resulting algorithms with DPs are asymptotically unbiased, so that the algorithms are provably convergent. Furthermore, we derive convergence rates to establish the superiority of the first-order and second-order algorithms, for the special case of a convex and quadratic optimization problem, respectively. Numerical experiments are used to validate the theoretical results.
Keyword:
Perturbation methods
Noise measurement
Signal processing algorithms
Linear programming
Convergence
Approximation algorithms
Optimization
Random directions stochastic approximation (RDSA)
simultaneous perturbation stochastic approximation (SPSA)
stochastic approximation
stochastic optimization
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期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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