Stochastic optimization refers to the minimization (or maximization) of a function in the presence of randomness in the optimization process. The randomness may be present as either noise in measurements or Monte Carlo randomness in the search procedure, or both.

Common methods of stochastic optimization include direct search methods (such as the Nelder-Mead method), stochastic approximation, stochastic programming, and miscellaneous methods such as simulated annealing and genetic algorithms.