Stochastic approximation: Difference between revisions

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In a nutshell, stochastic approximation algorithms deal with a function of the form <math display="inline"> f(\theta) = \operatorname E_{\xi} [F(\theta,\xi)] </math>

which is the expected value of a function depending on a [[random variable]] <math display="inline">\xi </math>. The goal is to recover properties of such a function <math display="inline">f</math> wit<ref name=":1" />outwithout evaluating it directly. Instead, stochastic approximation algorithms use random samples of <math display="inline">F(\theta,\xi)</math> to efficiently approximate properties of <math display="inline">f</math> such as zeros or extrema.

Recently, stochastic approximations have found extensive applications in the