Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
478701 | European Journal of Operational Research | 2010 | 19 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Pavlo A. Krokhmal, Policarpio Soberanis,