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- W77774003 abstract "My dissertation considers solving of linear programming problems with p-order conic constraints that are related to a class of stochastic optimization models with risk objective or constraints that involve higher moments of loss distributions. The general proposed approach is based on construction of polyhedral approximations for p-order cones, thereby approximating the non-linear convex p -order conic programming problems using linear programming models. It is shown that the resulting LP problems possess a special structure that makes them amenable to efficient decomposition techniques. The developed algorithms are tested on the example of portfolio optimization problem with higher moment coherent risk measures that reduces to a p-order conic programming problem. The conducted case studies on real financial data demonstrate that the proposed computational techniques compare favorably against a number of benchmark methods, including second-order conic programming methods." @default.
- W77774003 created "2016-06-24" @default.
- W77774003 creator A5020401417 @default.
- W77774003 date "2018-11-29" @default.
- W77774003 modified "2023-10-11" @default.
- W77774003 title "Risk optimization with p-order conic constraints" @default.
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- W77774003 doi "https://doi.org/10.17077/etd.f0qk8j24" @default.
- W77774003 hasPublicationYear "2018" @default.
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