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A Guide to Sample Average Approximation

In: Handbook of Simulation Optimization

Author

Listed:
  • Sujin Kim

    (National University of Singapore)

  • Raghu Pasupathy

    (Purdue University)

  • Shane G. Henderson

    (Cornell University)

Abstract

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.

Suggested Citation

  • Sujin Kim & Raghu Pasupathy & Shane G. Henderson, 2015. "A Guide to Sample Average Approximation," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 207-243, Springer.
    • Handle: RePEc:spr:isochp:978-1-4939-1384-8_8
    DOI: 10.1007/978-1-4939-1384-8_8
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