A stochastic optimization algorithm based on Newton-type method

An algorithm is presented for optimization problems in which the objective function, its gradient, and its Hessian would require Monte-Carlo-type simulations. First, a conceptual algorithm is presented. Then, an implementable version of this conceptual algorithm, based on the idea of Newton's method, is given, together with convergence results and the conditions needed to achieve convergence.<
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