Conjugate Gradient Methods as Modifications of the Standard Schemes

Neculai Andrei ()
Additional contact information
Neculai Andrei: Academy of Romanian Scientists

Chapter Chapter 7 in Nonlinear Conjugate Gradient Methods for Unconstrained Optimization, 2020, pp 205-247 from Springer

Abstract: Abstract Due to their simplicity and low memory requirements, conjugate gradient methods represent an important contribution to the class of methods for solving unconstrained optimization problems. These methods have good convergence properties and their iterations do not involve any matrices, making them extremely attractive for solving large-scale problems.

Date: 2020
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:978-3-030-42950-8_7

Ordering information: This item can be ordered from
http://www.springer.com/9783030429508

DOI: 10.1007/978-3-030-42950-8_7

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 ().