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

Pavlo A. Krokhmal, Policarpio Soberanis

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)653-671
Number of pages19
JournalEuropean Journal of Operational Research
Issue number3
StatePublished - Mar 16 2010


  • Polyhedral approximation
  • Portfolio optimization
  • Risk measures
  • Second-order conic programming
  • Stochastic programming
  • p-order conic programming

ASJC Scopus subject areas

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


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