Thus, SQG methods combine simulation models and (stochastic) optimization procedures in a single decision-making framework. SQG methods can be used in a wide range of practical problems, which are difficult or impossible to be solved by traditional optimization approaches. There are at least three main application areas for SQG methods:
SQG methods solve in an iterative way, thus requiring modest computer resources allocated per iteration, and reach with reasonable speed the vicinity of optimal solutions, with an accuracy that is sufficient for many application.
The implementation of the method is available on request.
On-going SQG-related research activities
Robust food-energy-water-land use NEXUS management for sustainable development: ASA, ESM, ENE, WAT develop new SQG-based approaches for iterative linkage of distributed models under uncertainty and asymmetric information to derive robust integrative solutions across sectors and regions.
Robust Disaster Risk Reduction (rDRR) mechanisms: A joint research initiative between IIASA RAV, ASA, and ESM Programs focuses on the development of SQG-based optimization approach for the design of optimal and robust disaster risk management financing programs. The optimization models are intended to be used to advise developing countries regarding better allocation of fiscal resources across different risk reduction and transfer instruments.
Simulation and optimization of stochastic (SOS_Water) water resource management: IIASA cross-program initiative between ASA, WAT, and ESM develops novel methods combining simulation and stochastic optimization for optimal and robust water-food-energy-environmental NEXUS management under uncertainty and resource scarcity.
Recent SQG-based models:
Both models are available on request.
Last edited: 14 November 2017
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Ermoliev Y (2009). Stochastic quasigradient methods: Applications. In: Encyclopedia of Optimization. Eds. Floudas, C.A. & Pardalos, P.M., New York: Springer-Verlag. ISBN 978-0-387-74758-310.1007/978-0-387-74759-0_663.
Ermoliev Y, Ermolieva T, Fischer G, Makowski M , Nilsson S, & Obersteiner M (2008). Discounting, catastrophic risks management and vulnerability modeling. Mathematics and Computers in Simulation 79 (4): 917-924. DOI:10.1016/j.matcom.2008.02.004.
Ermolieva T & Ermoliev Y (2005). Catastrophic risk management: flood and seismic risks case studies. In: Applications of Stochastic Programming. Eds. Wallace, S.W. & Ziemba, W.T., Philadelphia: MPS-SIAM Series on Optimization.
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