Stochastic Optimization

Stochastic optimization is a process that generates and utilizes random variables to optimize a specific objective function. This method typically proceeds in an iterative manner, gradually searching for the minimum or maximum of the objective function. Stochastic optimization is applicable in non-convex function spaces, where traditional deterministic optimization methods such as linear programming or quadratic programming are ineffective. Its core lies in enhancing optimization efficiency and robustness through random exploration, making it widely valuable in various applications.