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Non-smooth optimization methods for computation of the conditional value-at-risk and portfolio optimization

Beliakov, Gleb and Bagirov, Adil 2006, Non-smooth optimization methods for computation of the conditional value-at-risk and portfolio optimization, Optimization, vol. 55, no. 5&6, pp. 459-479.

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Title Non-smooth optimization methods for computation of the conditional value-at-risk and portfolio optimization
Author(s) Beliakov, Gleb
Bagirov, Adil
Journal name Optimization
Volume number 55
Issue number 5&6
Start page 459
End page 479
Publisher Taylor & Francis
Place of publication London, England
Publication date 2006-10
ISSN 1029-4945
0233-1934
Keyword(s) mathematics subject classifications 2000
90C05
90C30
90C56
91B28
Summary We examine numerical performance of various methods of calculation of the Conditional Value-at-risk (CVaR), and portfolio optimization with respect to this risk measure. We concentrate on the method proposed by Rockafellar and Uryasev in (Rockafellar, R.T. and Uryasev, S., 2000, Optimization of conditional value-at-risk. Journal of Risk, 2, 21-41), which converts this problem to that of convex optimization. We compare the use of linear programming techniques against a non-smooth optimization method of the discrete gradient, and establish the supremacy of the latter. We show that non-smooth optimization can be used efficiently for large portfolio optimization, and also examine parallel execution of this method on computer clusters.
Notes This is an electronic version of an article published in Optimization, Volume 55, Issue 5 & 6 2006 , pages 459 - 479 Optimization is available online at: http://www.informaworld.com/openurl?genre=article&issn=1029-4945&volume=55&issue=5&6&spage=459
Language eng
Field of Research 010303 Optimisation
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2006, Taylor & Francis
Persistent URL http://hdl.handle.net/10536/DRO/DU:30015938

Document type: Journal Article
Collections: School of Engineering and Information Technology
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