Risk optimization with p-order conic constraints: A linear programming approach

Pavlo A. Krokhmal and Policarpio Soberanis

European Journal of Operational Research, 2010, vol. 201, issue 3, 653-671

Abstract: 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.

Keywords: p-order; conic; programming; Second-order; conic; programming; Polyhedral; approximation; Risk; measures; Stochastic; programming; Portfolio; optimization (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:201:y:2010:i:3:p:653-671

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