Optimization of complex simulation models with stochastic gradient methods

Abstract

We describe the structure of stochastic optimization solver SQG (Stochastic QuasiGradient), which implements stochastic gradient methods for 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, stochastic bilevel problems where each level may require the solution of stochastic optimization problem or finding Nash equilibrium. We provide several complex examples with applications to water resources management, energy markets, pricing of services on social networks.