Local Optimization

Clemens Heitzinger ()
Additional contact information
Clemens Heitzinger: Technische Universität Wien, Center for Artificial Intelligence and Machine Learning (CAIML) and Department of Mathematics and Geoinformation

Chapter Chapter 12 in Algorithms with JULIA, 2022, pp 329-361 from Springer

Abstract: Abstract Optimization theory and algorithms can take advantage of the smoothness of real-valued functions. The underlying assumption is that a reasonable starting point sufficiently close to a local extremum is already known, for example from performing a global optimization, and that (at least) the gradient of the objective function is available. After a discussion of the convergence rates of gradient descent, accelerated gradient descent, and the Newton method, the bfgs method is presented in detail, as it is one of the most popular quasi-Newton methods and highly effective in practice.

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:sprchp:978-3-031-16560-3_12

Ordering information: This item can be ordered from
http://www.springer.com/9783031165603

DOI: 10.1007/978-3-031-16560-3_12

Access Statistics for this chapter

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