Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
A one-measurement form of simultaneous perturbation stochastic approximation
Automatica (Journal of IFAC)
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Universal parameter optimisation in games based on SPSA
Machine Learning
SPSA algorithms with measurement reuse
Proceedings of the 38th conference on Winter simulation
Stochastic gradient estimation using a single design point
Proceedings of the 38th conference on Winter simulation
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We study the convergence and asymptotic normality of a generalized form of stochastic approximation algorithm with deterministic perturbation sequences. Both one-simulation and two-simulation methods are considered. Assuming a special structure of deterministic sequence, we establish sufficient condition on the noise sequence for a.s. convergence of the algorithm. Construction of such a special structure of deterministic sequence follows the discussion of asymptotic normality. Finally we discuss ideas on further research in analysis and design of the deterministic perturbation sequences.