IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v265y2018i1d10.1007_s10479-017-2720-z.html
   My bibliography  Save this article

LP-based tractable subcones of the semidefinite plus nonnegative cone

Author

Listed:
  • Akihiro Tanaka

    (University of Tsukuba)

  • Akiko Yoshise

    (University of Tsukuba)

Abstract

The authors in a previous paper devised certain subcones of the semidefinite plus nonnegative cone and showed that satisfaction of the requirements for membership of those subcones can be detected by solving linear optimization problems (LPs) with O(n) variables and $$O(n^2)$$ O ( n 2 ) constraints. They also devised LP-based algorithms for testing copositivity using the subcones. In this paper, they investigate the properties of the subcones in more detail and explore larger subcones of the positive semidefinite plus nonnegative cone whose satisfaction of the requirements for membership can be detected by solving LPs. They introduce a semidefinite basis (SD basis) that is a basis of the space of $$n \times n$$ n × n symmetric matrices consisting of $$n(n+1)/2$$ n ( n + 1 ) / 2 symmetric semidefinite matrices. Using the SD basis, they devise two new subcones for which detection can be done by solving LPs with $$O(n^2)$$ O ( n 2 ) variables and $$O(n^2)$$ O ( n 2 ) constraints. The new subcones are larger than the ones in the previous paper and inherit their nice properties. The authors also examine the efficiency of those subcones in numerical experiments. The results show that the subcones are promising for testing copositivity as a useful application.

Suggested Citation

  • Akihiro Tanaka & Akiko Yoshise, 2018. "LP-based tractable subcones of the semidefinite plus nonnegative cone," Annals of Operations Research, Springer, vol. 265(1), pages 155-182, June.
  • Handle: RePEc:spr:annopr:v:265:y:2018:i:1:d:10.1007_s10479-017-2720-z
    DOI: 10.1007/s10479-017-2720-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-017-2720-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-017-2720-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Deng, Zhibin & Fang, Shu-Cherng & Jin, Qingwei & Xing, Wenxun, 2013. "Detecting copositivity of a symmetric matrix by an adaptive ellipsoid-based approximation scheme," European Journal of Operational Research, Elsevier, vol. 229(1), pages 21-28.
    2. Peter Dickinson, 2014. "On the exhaustivity of simplicial partitioning," Journal of Global Optimization, Springer, vol. 58(1), pages 189-203, January.
    3. F. Alizadeh, 2012. "An Introduction to Formally Real Jordan Algebras and Their Applications in Optimization," International Series in Operations Research & Management Science, in: Miguel F. Anjos & Jean B. Lasserre (ed.), Handbook on Semidefinite, Conic and Polynomial Optimization, chapter 0, pages 297-337, Springer.
    4. Bomze, Immanuel M., 2012. "Copositive optimization – Recent developments and applications," European Journal of Operational Research, Elsevier, vol. 216(3), pages 509-520.
    5. Julia Sponsel & Stefan Bundfuss & Mirjam Dür, 2012. "An improved algorithm to test copositivity," Journal of Global Optimization, Springer, vol. 52(3), pages 537-551, March.
    6. Peter Dickinson & Luuk Gijben, 2014. "On the computational complexity of membership problems for the completely positive cone and its dual," Computational Optimization and Applications, Springer, vol. 57(2), pages 403-415, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuzhu Wang & Akihiro Tanaka & Akiko Yoshise, 2021. "Polyhedral approximations of the semidefinite cone and their application," Computational Optimization and Applications, Springer, vol. 78(3), pages 893-913, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bomze, Immanuel M. & Gabl, Markus, 2023. "Optimization under uncertainty and risk: Quadratic and copositive approaches," European Journal of Operational Research, Elsevier, vol. 310(2), pages 449-476.
    2. Mohammadreza Safi & Seyed Saeed Nabavi & Richard J. Caron, 2021. "A modified simplex partition algorithm to test copositivity," Journal of Global Optimization, Springer, vol. 81(3), pages 645-658, November.
    3. Haibin Chen & Zheng-Hai Huang & Liqun Qi, 2017. "Copositivity Detection of Tensors: Theory and Algorithm," Journal of Optimization Theory and Applications, Springer, vol. 174(3), pages 746-761, September.
    4. Orizon Pereira Ferreira & Yingchao Gao & Sándor Zoltán Németh & Petra Renáta Rigó, 2024. "Gradient projection method on the sphere, complementarity problems and copositivity," Journal of Global Optimization, Springer, vol. 90(1), pages 1-25, September.
    5. Zhijian Lai & Akiko Yoshise, 2022. "Completely positive factorization by a Riemannian smoothing method," Computational Optimization and Applications, Springer, vol. 83(3), pages 933-966, December.
    6. Gabriele Eichfelder & Patrick Groetzner, 2022. "A note on completely positive relaxations of quadratic problems in a multiobjective framework," Journal of Global Optimization, Springer, vol. 82(3), pages 615-626, March.
    7. Carmo Brás & Gabriele Eichfelder & Joaquim Júdice, 2016. "Copositivity tests based on the linear complementarity problem," Computational Optimization and Applications, Springer, vol. 63(2), pages 461-493, March.
    8. Bo Zhang & YueLin Gao & Xia Liu & XiaoLi Huang, 2023. "Outcome-space branch-and-bound outer approximation algorithm for a class of non-convex quadratic programming problems," Journal of Global Optimization, Springer, vol. 86(1), pages 61-92, May.
    9. Paula Alexandra Amaral & Immanuel M. Bomze, 2019. "Nonconvex min–max fractional quadratic problems under quadratic constraints: copositive relaxations," Journal of Global Optimization, Springer, vol. 75(2), pages 227-245, October.
    10. Gizem Sağol & E. Yıldırım, 2015. "Analysis of copositive optimization based linear programming bounds on standard quadratic optimization," Journal of Global Optimization, Springer, vol. 63(1), pages 37-59, September.
    11. Haibin Chen & Zheng-Hai Huang & Liqun Qi, 2018. "Copositive tensor detection and its applications in physics and hypergraphs," Computational Optimization and Applications, Springer, vol. 69(1), pages 133-158, January.
    12. Anwa Zhou & Jinyan Fan, 2015. "Interiors of completely positive cones," Journal of Global Optimization, Springer, vol. 63(4), pages 653-675, December.
    13. Cheng Lu & Zhibin Deng & Qingwei Jin, 2017. "An eigenvalue decomposition based branch-and-bound algorithm for nonconvex quadratic programming problems with convex quadratic constraints," Journal of Global Optimization, Springer, vol. 67(3), pages 475-493, March.
    14. Jinyan Fan & Anwa Zhou, 2016. "Computing the distance between the linear matrix pencil and the completely positive cone," Computational Optimization and Applications, Springer, vol. 64(3), pages 647-670, July.
    15. Jinyan Fan & Jiawang Nie & Anwa Zhou, 2019. "Completely Positive Binary Tensors," Mathematics of Operations Research, INFORMS, vol. 44(3), pages 1087-1100, August.
    16. Immanuel M. Bomze & Vaithilingam Jeyakumar & Guoyin Li, 2018. "Extended trust-region problems with one or two balls: exact copositive and Lagrangian relaxations," Journal of Global Optimization, Springer, vol. 71(3), pages 551-569, July.
    17. Yuzhu Wang & Akihiro Tanaka & Akiko Yoshise, 2021. "Polyhedral approximations of the semidefinite cone and their application," Computational Optimization and Applications, Springer, vol. 78(3), pages 893-913, April.
    18. Wong, Man Hong & Zhang, Shuzhong, 2014. "On distributional robust probability functions and their computations," European Journal of Operational Research, Elsevier, vol. 233(1), pages 23-33.
    19. Riley Badenbroek & Etienne de Klerk, 2022. "An Analytic Center Cutting Plane Method to Determine Complete Positivity of a Matrix," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 1115-1125, March.
    20. O. I. Kostyukova & T. V. Tchemisova, 2022. "On strong duality in linear copositive programming," Journal of Global Optimization, Springer, vol. 83(3), pages 457-480, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:265:y:2018:i:1:d:10.1007_s10479-017-2720-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.