Discrete Gradient Methods

Adil Bagirov (), Napsu Karmitsa () and Marko M. Mäkelä ()
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Adil Bagirov: School of Information Technology and Mathematical Sciences, University of Ballarat
Napsu Karmitsa: University of Turku
Marko M. Mäkelä: University of Turku

Chapter Chapter 15 in Introduction to Nonsmooth Optimization, 2014, pp 327-333 from Springer

Abstract: Abstract In this chapter, we introduce two discrete gradient methods that can be considered as semi-derivative free methods in a sense that they do not use subgradient information and they do not approximate the subgradient but at the end of the solution process (i.e., near the optimal point). The introduced methods are the original discrete gradient method for small-scale nonsmooth optimization and its limited memory bundle version the limited memory discrete gradient bundle method for medium- and semi-large problems.

Keywords: Search Direction; Outer Iteration; Descent Direction; Iteration Point; Bundle Method (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-08114-4_15

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DOI: 10.1007/978-3-319-08114-4_15

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