Stochastic algorithms

Algorithms:


Introduction

Stochastic optimization refers to a collection of methods for minimizing or maximizing an objective function when randomness is present. Over the last few decades these methods have become essential tools for science, engineering, business, computer science, and statistics. Specific applications are varied, but include: running simulations to refine the placement of acoustic sensors on a beam, deciding when to release water from a reservoir for hydroelectric power generation, and optimizing the parameters of a statistical model for a given data set. Randomness usually enters the problem in two ways: through the cost function or the constraint set. Although stochastic optimization refers to any optimization method that employs randomness within some communities, we only consider settings where the objective function or constraints are random.