### Abstract

This paper discusses the use of polyhedral approximations in solving p-order cone programming (pOCP) problems, or linear problems with p-order cone constraints, and their mixed-integer extensions. In particular, it is shown that the cutting-plane technique proposed in Krokhmal and Soberanis [Risk optimization with p-order conic constraints: A linear programming approach, Eur. J. Oper. Res. 201 (2010), pp. 653-671, http://dx.doi.org/10.1016/j.ejor.2009. 03.053] for a special type of polyhedral approximations of pOCP problems, which allows for generation of cuts in constant time not dependent on the accuracy of approximation, is applicable to a larger family of polyhedral approximations. We also show that it can further be extended to form an exact solution method for pOCP problems with O(ε^{-1}) iteration complexity. Moreover, it is demonstrated that an analogous constant-time cut-generating algorithm exists for recursively constructed lifted polyhedral approximations of second-order cones due to Ben-Tal and Nemirovski [On polyhedral approximations of the second-order cone, Math. Oper. Res. 26 (2001), pp. 193-205. Available at http://dx.doi.org/ 10.1287/moor.26.2.193.10561]. It is also shown that the developed polyhedral approximations and the corresponding cutting-plane solution methods can be efficiently used for obtaining exact solutions of mixed-integer pOCP problems.

Original language | English (US) |
---|---|

Pages (from-to) | 1210-1237 |

Number of pages | 28 |

Journal | Optimization Methods and Software |

Volume | 29 |

Issue number | 6 |

DOIs | |

State | Published - Nov 2 2014 |

Externally published | Yes |

### Keywords

- cutting-plane methods
- mixed-integer p-order cone programming
- p-order cone programming
- polyhedral approximation
- portfolio optimization
- second-order cone programming
- stochastic programming

### ASJC Scopus subject areas

- Software
- Control and Optimization
- Applied Mathematics

## Fingerprint Dive into the research topics of 'Polyhedral approximations in p-order cone programming'. Together they form a unique fingerprint.

## Cite this

*Optimization Methods and Software*,

*29*(6), 1210-1237. https://doi.org/10.1080/10556788.2013.877905