Optimization of Simulation Models and other Complex Problems with Stochastic Gradient Methods

In this chapter, we describe, the structure of the stochastic optimization solver SQG (Stochastic QuasiGradient), which implements stochastic gradient methods for the optimization of complex stochastic simulation models. The solver finds the equilibrium solution when the simulation model describes the system with several actors. The solver is parallelizable and it performs several simulation threads in parallel. It is capable of solving stochastic optimization problems, finding stochastic Nash equilibria, and stochastic bilevel problems where each level may require the solution of a stochastic optimization problem or finding Nash equilibrium. We provide several complex examples with applications to water resources management, energy markets, and pricing of services on social networks.