Risk optimization with p-order conic constraints: A linear programming approach
The paper considers solving of linear programming problems with p-order conic constraints that are related to a certain class of stochastic optimization models with risk objective or constraints. The proposed approach is based on construction of polyhedral approximations for p-order cones, and then invoking a Benders decomposition scheme that allows for efficient solving of the approximating problems. The conducted case study of portfolio optimization with p-order conic constraints demonstrates that the developed computational techniques compare favorably against a number of benchmark methods, including second-order conic programming methods.
Year of publication: |
2010
|
---|---|
Authors: | Krokhmal, Pavlo A. ; Soberanis, Policarpio |
Published in: |
European Journal of Operational Research. - Elsevier, ISSN 0377-2217. - Vol. 201.2010, 3, p. 653-671
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Publisher: |
Elsevier |
Keywords: | p-order conic programming Second-order conic programming Polyhedral approximation Risk measures Stochastic programming Portfolio optimization |
Saved in:
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