A Guide to Sample Average Approximation

  • Sujin Kim
  • Raghu Pasupathy
  • Shane G. HendersonEmail author
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 216)


This chapter reviews the principles of sample average approximation (SAA) for solving simulation optimization problems. We provide an accessible overview of the area and survey interesting recent developments. We explain when one might want to use SAA and when one might expect it to provide good-quality solutions. We also review some of the key theoretical properties of the solutions obtained through SAA. We contrast SAA with stochastic approximation (SA) methods in terms of the computational effort required to obtain solutions of a given quality, explaining why SA “wins” asymptotically. However, an extension of SAA known as retrospective optimization can match the asymptotic convergence rate of SA, at least up to a multiplicative constant.



This work was partially supported by National Science Foundation grants CMMI-0800688 and CMMI-1200315, and by Singapore MOE Academic Research Fund grant WBS R-266-000-049-133.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Sujin Kim
    • 1
  • Raghu Pasupathy
    • 2
  • Shane G. Henderson
    • 3
    Email author
  1. 1.National University of SingaporeSingaporeSingapore
  2. 2.Purdue UniversityWest LafayetteUSA
  3. 3.Cornell UniversityIthacaUSA

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