Nonadaptive Univariate Optimization for Observations with Noise

James M. Calvin ()
Additional contact information
James M. Calvin: New Jersey Institute of Technology

A chapter in Models and Algorithms for Global Optimization, 2007, pp 185-192 from Springer

Abstract: Abstract It is much more difficult to approximate the minimum of a function using noise-corrupted function evaluations than when the function can be evaluated precisely. This chapter is concerned with the question of exactly how much harder it is in a particular setting; namely, on average when the objective function is a Wiener process, the noise is independent Gaussian, and nonadaptive algorithms are considered.

Keywords: Global Optimization; Conditional Distribution; Wiener Process; Uniform Grid; Regular Sequence (search for similar items in EconPapers)
Date: 2007
References: Add references at CitEc
Citations: View citations in EconPapers (2)

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:spochp:978-0-387-36721-7_12

Ordering information: This item can be ordered from
http://www.springer.com/9780387367217

DOI: 10.1007/978-0-387-36721-7_12

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().