A Guide to Sample Average Approximation

Sujin Kim (), Raghu Pasupathy () and Shane G. Henderson ()
Additional contact information
Sujin Kim: National University of Singapore
Raghu Pasupathy: Purdue University
Shane G. Henderson: Cornell University

Chapter Chapter 8 in Handbook of Simulation Optimization, 2015, pp 207-243 from Springer

Abstract: 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.

Keywords: Sample Average Approximation (SAA); Retrospective Optimization; Stochastic Approximation (SA); Infinitesimal Perturbation Analysis (IPA); SAA Problem (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (36)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-1-4939-1384-8_8

Ordering information: This item can be ordered from
http://www.springer.com/9781493913848

DOI: 10.1007/978-1-4939-1384-8_8

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().