A Stochastic Approximation Method

Метод стохастической аппроксимации
Herbert Robbins, Sutton Monro
1951-09-01

convergence in probabilitymonotone response functionroot-findingstochastic approximationsuccessive experiments
Let $M(x)$ denote the expected value at level $x$ of the response to a certain experiment. $M(x)$ is assumed to be a monotone function of $x$ but is unknown to the experimenter, and it is desired to find the solution $x = \theta$ of the equation $M(x) = \alpha$, where $\alpha$ is a given constant. We give a method for making successive experiments at levels $x_1,x_2,\cdots$ in such a way that $x_n$ will tend to $\theta$ in probability.
1
Assumes only that the response expectation M(x) is monotone, while its functional form is unknown to the experimenter.
2
Introduces a stochastic approximation method for locating the unknown solution θ of M(x)=α through sequential experiments.
3
Provides a sequential approach for estimating a target level defined by an expected experimental response.
4
Specifies adaptive experiment levels x₁, x₂, … designed so that the sequence xₙ converges in probability to θ.

The unknown monotone response function M(x) of the experiment and its target level θ satisfying M(θ)=α

The probabilistic convergence of successive experimental levels x_n to the solution θ of M(x)=α

Publication Details
Publication Date
1951-09-01
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Authors
Herbert Robbins
Sutton Monro
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