Convergence of Implementable Descent Algorithms for Unconstrained Optimization

J. P. Dussault
Additional contact information
J. P. Dussault: Université de Sherbrooke

Journal of Optimization Theory and Applications, 2000, vol. 104, issue 3, No 12, 739-745

Abstract: Abstract Descent algorithms use sufficient descent directions combined with stepsize rules, such as the Armijo rule, to produce sequences of iterates whose cluster points satisfy some necessary optimality conditions. In this note, we present a proof that the whole sequence of iterates converges for quasiconvex objective functions.

Keywords: descent algorithms; global convergence (search for similar items in EconPapers)
Date: 2000
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1023/A:1004602012151 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:joptap:v:104:y:2000:i:3:d:10.1023_a:1004602012151

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1023/A:1004602012151

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().