Optimal random perturbations for stochastic approximation using a simultaneous perturbation gradient approximation
نویسندگان
چکیده
منابع مشابه
Correction to "Optimal random perturbations for stochastic approximation using a simultaneous perturbation gradient approximation"
The simultaneous perturbation stochastic approximation (SPSA) algorithm has recently attracted considerable attention for optimization problems where it is di cult or impossible to obtain a direct gradient of the objective (say, loss) function. The approach is based on a highly e cient simultaneous perturbation approximation to the gradient based on loss function measurements. SPSA is based on ...
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Stochastic approximation as a method of simulation optimization is well-studied and numerous practical applications exist. One approach, simultaneous perturbation stochastic approximation (SPSA), has proven to be an efficient algorithm for such purposes. SPSA uses a centered difference approximation to the gradient based on two function evaluations regardless of the dimension of the problem. It...
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A simultaneous perturbation stochastic approximation (SPSA) method has been developed in this paper, using the operators of perturbation with the Lipschitz density function. This model enables us to use the approximation of the objective function by twice differentiable functions and to present their gradients by volume integrals. The calculus of the stochastic gradient by means of this present...
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ژورنال
عنوان ژورنال: IEEE Transactions on Automatic Control
سال: 1998
ISSN: 0018-9286
DOI: 10.1109/9.720513