This is a critical review of recent results in stochastic approximation methods for optimization problems. Part I deals with unconstrained optimization, with special emphasis on the problem of choice of the direction and the length of iteration steps.
This is a critical review of recent results in stochastic approximation methods for optimization problems. Part II deals with constrained optimization; here, the stochastic variants of the penalty function method, the method of feasible directions and the method of Lagrange functions are discussed.
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