IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v55y2013i2p209-226.html
   My bibliography  Save this article

Robust solutions of quadratic optimization over single quadratic constraint under interval uncertainty

Author

Listed:
  • V. Jeyakumar
  • G. Li

Abstract

In this paper we examine non-convex quadratic optimization problems over a quadratic constraint under unknown but bounded interval perturbation of problem data in the constraint and develop criteria for characterizing robust (i.e. uncertainty-immunized) global solutions of classes of non-convex quadratic problems. Firstly, we derive robust solvability results for quadratic inequality systems under parameter uncertainty. Consequently, we obtain characterizations of robust solutions for uncertain homogeneous quadratic problems, including uncertain concave quadratic minimization problems and weighted least squares. Using homogenization, we also derive characterizations of robust solutions for non-homogeneous quadratic problems. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • V. Jeyakumar & G. Li, 2013. "Robust solutions of quadratic optimization over single quadratic constraint under interval uncertainty," Journal of Global Optimization, Springer, vol. 55(2), pages 209-226, February.
    • Handle: RePEc:spr:jglopt:v:55:y:2013:i:2:p:209-226
    DOI: 10.1007/s10898-012-9857-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10898-012-9857-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10898-012-9857-8?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. Vaithilingam Jeyakumar & Zhiyou Wu, 2007. "Conditions For Global Optimality Of Quadratic Minimization Problems With Lmi Constraints," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 24(02), pages 149-160.
    2. B. T. Polyak, 1998. "Convexity of Quadratic Transformations and Its Use in Control and Optimization," Journal of Optimization Theory and Applications, Springer, vol. 99(3), pages 553-583, December.
    3. Dimitris Bertsimas & David B. Brown, 2009. "Constructing Uncertainty Sets for Robust Linear Optimization," Operations Research, INFORMS, vol. 57(6), pages 1483-1495, December.
    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. F. Domes & A. Goldsztejn, 2017. "A branch and bound algorithm for quantified quadratic programming," Journal of Global Optimization, Springer, vol. 68(1), pages 1-22, May.

    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. Guoyin Li, 2012. "Global Quadratic Minimization over Bivalent Constraints: Necessary and Sufficient Global Optimality Condition," Journal of Optimization Theory and Applications, Springer, vol. 152(3), pages 710-726, March.
    2. Christina Büsing & Sigrid Knust & Xuan Thanh Le, 2018. "Trade-off between robustness and cost for a storage loading problem: rule-based scenario generation," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 339-365, December.
    3. Wenqing Chen & Melvyn Sim & Jie Sun & Chung-Piaw Teo, 2010. "From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization," Operations Research, INFORMS, vol. 58(2), pages 470-485, April.
    4. Wu, Zhiyou & Tian, Jing & Ugon, Julien & Zhang, Liang, 2015. "Global optimality conditions and optimization methods for constrained polynomial programming problems," Applied Mathematics and Computation, Elsevier, vol. 262(C), pages 312-325.
    5. Chassein, André & Dokka, Trivikram & Goerigk, Marc, 2019. "Algorithms and uncertainty sets for data-driven robust shortest path problems," European Journal of Operational Research, Elsevier, vol. 274(2), pages 671-686.
    6. Güray Kara & Ayşe Özmen & Gerhard-Wilhelm Weber, 2019. "Stability advances in robust portfolio optimization under parallelepiped uncertainty," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(1), pages 241-261, March.
    7. Baringo, Luis & Boffino, Luigi & Oggioni, Giorgia, 2020. "Robust expansion planning of a distribution system with electric vehicles, storage and renewable units," Applied Energy, Elsevier, vol. 265(C).
    8. Roger J. A. Laeven & John G. M. Schoenmakers & Nikolaus F. F. Schweizer & Mitja Stadje, 2020. "Robust Multiple Stopping -- A Pathwise Duality Approach," Papers 2006.01802, arXiv.org, revised Sep 2021.
    9. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    10. Gabriel Haeser, 2017. "An Extension of Yuan’s Lemma and Its Applications in Optimization," Journal of Optimization Theory and Applications, Springer, vol. 174(3), pages 641-649, September.
    11. Gülpınar, Nalan & Pachamanova, Dessislava & Çanakoğlu, Ethem, 2013. "Robust strategies for facility location under uncertainty," European Journal of Operational Research, Elsevier, vol. 225(1), pages 21-35.
    12. Jianhui Du & Peng Wu & Yiqing Wang & Dan Yang, 2023. "Multi-stage humanitarian emergency logistics: robust decisions in uncertain environment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 899-922, January.
    13. Wei Chen & Liansheng Zhang, 2010. "Global optimality conditions for quadratic 0-1 optimization problems," Journal of Global Optimization, Springer, vol. 46(2), pages 191-206, February.
    14. Chassein, André & Goerigk, Marc, 2018. "Compromise solutions for robust combinatorial optimization with variable-sized uncertainty," European Journal of Operational Research, Elsevier, vol. 269(2), pages 544-555.
    15. Fabián Flores-Bazán & William Echegaray & Fernando Flores-Bazán & Eladio Ocaña, 2017. "Primal or dual strong-duality in nonconvex optimization and a class of quasiconvex problems having zero duality gap," Journal of Global Optimization, Springer, vol. 69(4), pages 823-845, December.
    16. C. Durieu & É. Walter & B. Polyak, 2001. "Multi-Input Multi-Output Ellipsoidal State Bounding," Journal of Optimization Theory and Applications, Springer, vol. 111(2), pages 273-303, November.
    17. V. Jeyakumar & G. Li & S. Srisatkunarajah, 2014. "Global optimality principles for polynomial optimization over box or bivalent constraints by separable polynomial approximations," Journal of Global Optimization, Springer, vol. 58(1), pages 31-50, January.
    18. de Klerk, E., 2006. "The Complexity of Optimizing over a Simplex, Hypercube or Sphere : A Short Survey," Discussion Paper 2006-85, Tilburg University, Center for Economic Research.
    19. Laeven, R.J.A. & Schoenmakers, John G.M. & Schweizer, Nikolaus & Stadje, M.A., 2024. "Robust multiple stopping — A duality approach," Other publications TiSEM 132c6688-3f07-47d8-a4dc-b, Tilburg University, School of Economics and Management.
    20. Mehdi Ansari & Juan S. Borrero & Leonardo Lozano, 2023. "Robust Minimum-Cost Flow Problems Under Multiple Ripple Effect Disruptions," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 83-103, January.

    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:jglopt:v:55:y:2013:i:2:p:209-226. 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.