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Convergence of the Nelder-Mead Simplex Algorithm for Unconstrained StochasticOptimization

机译：无约束随机优化的Nelder-mead单纯形算法的收敛性

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The Nelder-Mead simplex method is a direct search algorithm that has foundwidespread use in the optimization of nonlinear functions. Originally designed for deterministic optimization, the method is robust with respect to small perturbations in the function's values; and therefore, this method has been used for optimizing stochastic functions as well. However, if the random perturbations in the function's values are large enough the method may terminate before reaching the optimizer of the expected function. We prove convergence of the simplex to a point with probability 1 for constant functions with additive noise

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