- Title
- Introduction
- Creator
- Bagirov, Adil; Gaudioso, Manlio; Karmitsa, Napsu; Mäkelä, Marko; Taheri, Sona
- Date
- 2020
- Type
- Text; Book chapter
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/173745
- Identifier
- vital:14697
- Identifier
-
https://doi.org/10.1007/978-3-030-34910-3_1
- Identifier
- ISBN:9783030349103 (ISBN); 9783030349097 (ISBN)
- Abstract
- Nonsmooth optimization (NSO) is among the most challenging tasks in the field of mathematical programming. It addresses optimization problems where objective and/or constraint functions have discontinuous gradients. NSO problems arise in many real life applications. Moreover, some smooth optimization techniques like different decomposition methods, dual formulations and exact penalty methods may lead us to solve NSO problems being either smaller in dimension or simpler in structure. In addition, some optimization problems may be analytically smooth but numerically nonsmooth. This is the case, for instance, with noisy input data and so-called stiff problems, which are numerically unstable and behave like nonsmooth problems. © Springer Nature Switzerland AG 2020.
- Publisher
- Springer International Publishing
- Relation
- Numerical Nonsmooth Optimization: State of the Art Algorithms p. 1-16
- Rights
- Copyright © Springer Nature Switzerland AG 2020
- Rights
- This metadata is freely available under a CCO license
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