Stochastic Approximation Methods and Their Finite-Time Convergence Properties

Saeed Ghadimi () and Guanghui Lan ()
Additional contact information
Saeed Ghadimi: University of Florida
Guanghui Lan: University of Florida

Chapter Chapter 7 in Handbook of Simulation Optimization, 2015, pp 179-206 from Springer

Abstract: Abstract This chapter surveys some recent advances in the design and analysis of two classes of stochastic approximation methods: stochastic first- and zeroth-order methods for stochastic optimization. We focus on the finite-time convergence properties (i.e., iteration complexity) of these algorithms by providing bounds on the number of iterations required to achieve a certain accuracy. We point out that many of these complexity bounds are theoretically optimal for solving different classes of stochastic optimization problems.

Keywords: Finite-time Convergence Property; SA Method; Convex Stochastic Optimization Problem; Step Size Policy; Strong Convexity (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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_7

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

DOI: 10.1007/978-1-4939-1384-8_7

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 ().