Robbins-Monro Stochastic Approximation

A stochastic approximation method that functions by placing conditions on iterative step sizes and whose convergence is guaranteed under mild conditions. However, the method requires knowledge of the analytical gradient of the function under consideration.

Kiefer and Wolfowitz (1952) developed a finite difference version of the Robbins-Monro method which maintains the nice convergence properties, while obviating the need for knowledge of the analytic form of the gradient.

See also

Stochastic Approximation, Stochastic Optimization

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Kiefer, J. and Wolfowitz, J. "Stochastic Estimation of the Maximum of a Regression Function." Ann. Math. Stat. 23, 462-466, 1952.Robbins, H. and Munro, S. "A Stochastic Approximation Method." Ann. Math. Stat. 22, 400-407, 1951.

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Robbins-Monro Stochastic Approximation

Cite this as:

Weisstein, Eric W. "Robbins-Monro Stochastic Approximation." From MathWorld--A Wolfram Web Resource.

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