Direct Methods for Constrained Optimization

Neculai Andrei ()
Additional contact information
Neculai Andrei: Center for Advanced Modeling and Optimization

Chapter 20 in Modern Numerical Nonlinear Optimization, 2022, pp 679-689 from Springer

Abstract: Abstract As we have already seen in Chap. 9 , the direct methods for unconstrained optimization do not use derivative information. From the multitude of these methods, only the NELMEAD by Nelder and Mead (1965), NEWUOA by Powell (2004, 2006), and DEEPS by Andrei (2021a) have been discussed. They are suitable for solving unconstrained optimization problems with a small number of variables (let us say up to 100).

Date: 2022
References: Add references at CitEc
Citations:

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:978-3-031-08720-2_20

Ordering information: This item can be ordered from
http://www.springer.com/9783031087202

DOI: 10.1007/978-3-031-08720-2_20

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 ().