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[[Stochastic approximation]] is used when the function cannot be computed directly, only estimated via noisy observations. In this scenarios, this method (or family of methods) looks for the extrema of these function. The objective function would be:<ref>Powell, W. (2011). ''Approximate Dynamic Programming Solving the Curses of Dimensionality'' (2nd ed., Wiley Series in Probability and Statistics). Hoboken: Wiley.</ref>
:<math>\underset{\text{x}\in\theta}{\min}f\bigl(\text{x}\bigr) = \underset{\text{x}\in\theta}{\min}\Epsilon[F\bigl(\text{x,y})]</math>
:<math>y</math> is a random variable that represents the noise.
:<math>x</math> is the parameter that minimizes <math>f\bigl(\text{x}\bigr)</math>.
:<math>\theta</math> is the ___domain of the parameter <math>x</math>.
=== Derivative-free optimization methods ===
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