Constrained optimization via stochastic approximation with a simultaneous perturbation gradient approximation

摘要: This paper deals with a projection algorithm for stochastic approximation using simultaneous perturbation gradient optimization under inequality constraints where no direct of the loss function is available and are given as explicit functions parameters. It shown that, application algorithm, parameter iterate converges almost surely to Kuhn-Tucker point. The procedure illustrated by numerical example.