The paper considers solving of linear programmingproblems with p-orderconic constraints that are related to a certain class of stochastic optimization models with risk objective or constraints. The proposed approach...
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The paper considers solving of linear programmingproblems with p-orderconic 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-orderconic constraints demonstrates that the developed computational techniques compare favorably against a number of benchmark methods, including second-orderconicprogramming methods. (C) 2009 Elsevier B.V. All rights reserved.
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