Stochastic optimization is a field of study and a methodological approach concerned with solving optimization problems where some problem parameters are not known exactly but are represented by probability distributions or subject to random variations. It investigates methods for finding optimal solutions or decisions under uncertainty, typically employing iterative algorithms that make progress based on noisy or random information (e.g., samples or estimates of gradients). Its significance lies in providing frameworks and algorithms to address complex decision-making problems in diverse domains where randomness is a defining characteristic, such as finance, machine learning, operations research, and engineering.