### Abstract

In this work we present an extension of Chubanov’s algorithm to the case of homogeneous feasibility problems over a symmetric cone K. As in Chubanov’s method for linear feasibility problems, the algorithm consists of a basic procedure and a step where the solutions are confined to the intersection of a half-space and K. Following an earlier work by Kitahara and Tsuchiya on second order cone feasibility problems, progress is measured through the volumes of those intersections: when they become sufficiently small, we know it is time to stop. We never have to explicitly compute the volumes, it is only necessary to keep track of the reductions between iterations. We show this is enough to obtain concrete upper bounds to the minimum eigenvalues of a scaled version of the original feasibility problem. Another distinguishing feature of our approach is the usage of a spectral norm that takes into account the way that K is decomposed as simple cones. In several key cases, including semidefinite programming and second order cone programming, these norms make it possible to obtain better complexity bounds for the basic procedure when compared to a recent approach by Peña and Soheili. Finally, in the appendix, we present a translation of the algorithm to the homogeneous feasibility problem in semidefinite programming.

Language | English |
---|---|

Pages | 117-149 |

Number of pages | 33 |

Journal | Mathematical Programming |

Volume | 173 |

Issue number | 1-2 |

DOIs | |

Publication status | Published - Jan 23 2019 |

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### All Science Journal Classification (ASJC) codes

- Software
- Mathematics(all)

### Cite this

*Mathematical Programming*,

*173*(1-2), 117-149. https://doi.org/10.1007/s10107-017-1207-7

**An extension of Chubanov’s algorithm to symmetric cones.** / Lourenço, Bruno F.; Kitahara, Tomonari; Muramatsu, Masakazu; Tsuchiya, Takashi.

Research output: Contribution to journal › Article

*Mathematical Programming*, vol. 173, no. 1-2, pp. 117-149. https://doi.org/10.1007/s10107-017-1207-7

}

TY - JOUR

T1 - An extension of Chubanov’s algorithm to symmetric cones

AU - Lourenço, Bruno F.

AU - Kitahara, Tomonari

AU - Muramatsu, Masakazu

AU - Tsuchiya, Takashi

PY - 2019/1/23

Y1 - 2019/1/23

N2 - In this work we present an extension of Chubanov’s algorithm to the case of homogeneous feasibility problems over a symmetric cone K. As in Chubanov’s method for linear feasibility problems, the algorithm consists of a basic procedure and a step where the solutions are confined to the intersection of a half-space and K. Following an earlier work by Kitahara and Tsuchiya on second order cone feasibility problems, progress is measured through the volumes of those intersections: when they become sufficiently small, we know it is time to stop. We never have to explicitly compute the volumes, it is only necessary to keep track of the reductions between iterations. We show this is enough to obtain concrete upper bounds to the minimum eigenvalues of a scaled version of the original feasibility problem. Another distinguishing feature of our approach is the usage of a spectral norm that takes into account the way that K is decomposed as simple cones. In several key cases, including semidefinite programming and second order cone programming, these norms make it possible to obtain better complexity bounds for the basic procedure when compared to a recent approach by Peña and Soheili. Finally, in the appendix, we present a translation of the algorithm to the homogeneous feasibility problem in semidefinite programming.

AB - In this work we present an extension of Chubanov’s algorithm to the case of homogeneous feasibility problems over a symmetric cone K. As in Chubanov’s method for linear feasibility problems, the algorithm consists of a basic procedure and a step where the solutions are confined to the intersection of a half-space and K. Following an earlier work by Kitahara and Tsuchiya on second order cone feasibility problems, progress is measured through the volumes of those intersections: when they become sufficiently small, we know it is time to stop. We never have to explicitly compute the volumes, it is only necessary to keep track of the reductions between iterations. We show this is enough to obtain concrete upper bounds to the minimum eigenvalues of a scaled version of the original feasibility problem. Another distinguishing feature of our approach is the usage of a spectral norm that takes into account the way that K is decomposed as simple cones. In several key cases, including semidefinite programming and second order cone programming, these norms make it possible to obtain better complexity bounds for the basic procedure when compared to a recent approach by Peña and Soheili. Finally, in the appendix, we present a translation of the algorithm to the homogeneous feasibility problem in semidefinite programming.

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UR - http://www.scopus.com/inward/citedby.url?scp=85033606766&partnerID=8YFLogxK

U2 - 10.1007/s10107-017-1207-7

DO - 10.1007/s10107-017-1207-7

M3 - Article

VL - 173

SP - 117

EP - 149

JO - Mathematical Programming

T2 - Mathematical Programming

JF - Mathematical Programming

SN - 0025-5610

IS - 1-2

ER -