Subgradient 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 10 in Introduction to Nonsmooth Optimization, 2014, pp 295-297 from Springer

Abstract: Abstract The history of subgradient methods (Kiev methods) starts in the 1960s and they were mainly developed in the Soviet Union. The basic idea behind subgradient methods is to generalize smooth methods by replacing the gradient with an arbitrary subgradient. Due to this simple structure, they are widely used NSO methods, although they may suffer from some serious drawbacks (this is true especially with the simplest versions of subgradient methods). The first method to be considered in this chapter is the cornerstone of NSO, the standard subgradient method. Then the ideas of the more sophisticated subgradient method, the well-known Shor’s r-algorithm are introduced.

Keywords: Standard Subgradient Method; Arbitrary Subgradient; Smooth Method; Implementable Stopping Criterion; Predetermined Step Size (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/978-3-319-08114-4_10

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