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

Gaivoronski, A. & Ermoliev, Y. (2023). Optimization of Simulation Models and other Complex Problems with Stochastic Gradient Methods. In: Modern Optimization Methods for Decision Making Under Risk and Uncertainty. Eds. Gaivoronski, A., Knopov, P., & Zaslavskyi, V., pp. 1-14 Taylor & Francis. ISBN 9781003260196 10.1201/9781003260196-1.

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Abstract

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.

Item Type:	Book Section
Research Programs:	Advancing Systems Analysis (ASA) Advancing Systems Analysis (ASA) > Cooperation and Transformative Governance (CAT) Advancing Systems Analysis (ASA) > Exploratory Modeling of Human-natural Systems (EM)
Depositing User:	Luke Kirwan
Date Deposited:	29 Aug 2023 06:54
Last Modified:	29 Aug 2023 06:54
URI:	https://pure.iiasa.ac.at/19027

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