A review of simulation optimization techniques
By Sigrún Andradóttir. 1998
We present a review of methods for optimizing stochastic systems using simulation. The focus is on gradient based techniques for optimization with respect to continuous decision parameters and on random search methods for optimization with respect to discrete decision parameters.
Subfields: Mathematical Optimization
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In Mathematical Optimization
- Guanghui Lan
- Saeed Ghadimi
- Karol Gregor
- Marco Dorigo
- Daan Wierstra
- Walter J. Gutjahr
- Natasa Krejic
- Andriy Mnih
Citations in the corpus
- Deep AutoRegressive Networks (2013, methodology): “We use a Monte Carlo approximation to estimate ∇ θ E [ f ( H i )] where h i is sampled from q ( h i ) (Williams, 1992; Andrad´ottir, 1998).”
- Accelerated gradient methods for nonconvex nonlinear and stochastic programming (2013, neutral): “, in simulation-based optimization [1,2,9,19].”
- A unified framework for stochastic optimization (2019, background): “Other reviews inlude ( Andradóttir, 1998a; 1998b; Azadivar, 1999 ), Fu (2002) , and im and Nelson (2007) .”
- Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization (2013, background): “For some examples which fit our setting, one may refer the problems in references [1,11,12,15,16,21,22,23,32].”
- A survey on metaheuristics for stochastic combinatorial optimization (2009, methodology): “A compact picture of the field is given by the reviews of the Winter Simulation Conference (Andradóttir 1998; O ´ lafsson and Kim 2002).”
- A simulation based optimization approach to supply chain management under demand uncertainty (2004, background): “Reviews of the current research on simulation-based optimization developments can be found in Carson and Maria (1997) and Andradottir (1998).”
- Line search methods with variable sample size for unconstrained optimization (2013, background): “The approximate gradient values in noisy environment are complicated issue and a lot of research is devoted to that topic, see [1, 8, 9, 12, 19].”