- Title
- Discrete gradient methods
- Creator
- Bagirov, Adil; Taheri, Sona; Karmitsa, Napsu
- Date
- 2020
- Type
- Text; Book chapter
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/173735
- Identifier
- vital:14699
- Identifier
-
https://doi.org/10.1007/978-3-030-34910-3_18
- Identifier
- ISBN:9783030349103 (ISBN); 9783030349097 (ISBN)
- Abstract
- In this chapter, the notion of a discrete gradient is introduced and it is shown that the discrete gradients can be used to approximate subdifferentials of a broad class of nonsmooth functions. Two methods based on such approximations, more specifically, the discrete gradient method (DGM) and its limited memory version (LDGB), are described. These methods are semi derivative-free methods for solving nonsmooth and, in general, nonconvex optimization problems. The performance of the methods is demonstrated using some academic test problems. © Springer Nature Switzerland AG 2020.
- Publisher
- Springer International Publishing
- Relation
- Numerical Nonsmooth Optimization: State of the Art Algorithms p. 621-654
- Rights
- Copyright © Springer Nature Switzerland AG 2020
- Rights
- This metadata is freely available under a CCO license
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