UNCONSTRAINED OPTIMIZATION

Richard W. Cottle () and Mukund N. Thapa
Additional contact information
Richard W. Cottle: Stanford University
Mukund N. Thapa: Optical Fusion, Inc.

Chapter Chapter 9 in Linear and Nonlinear Optimization, 2017, pp 271-316 from Springer

Abstract: Abstract The previous chapter presented a small sample of nonlinear optimization problems that might occur in practice; some of these had constraints on their variables and others did not. From this chapter onwards we will discuss ways to solve various types of optimization problems stated in terms of minimization. This is not really restrictive for we have already shown in (8.3) how to convert maximization problems to minimization problems. Our discussion will emphasize algorithms to solve nonlinear minimization problems and develop ways to determine if, in practice, the algorithms described are behaving as expected. Moreover, our discussion will strive to provide an understanding of optimality criteria, how they motivate the development of algorithms, and how they are used in determining if an optimal solution has been found.

Keywords: Stationary Point; Nonlinear Optimization; Search Direction; Unconstrained Optimization; Descent Direction (search for similar items in EconPapers)
Date: 2017
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:isochp:978-1-4939-7055-1_9

Ordering information: This item can be ordered from
http://www.springer.com/9781493970551

DOI: 10.1007/978-1-4939-7055-1_9

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 ().