IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v187y2020i2d10.1007_s10957-020-01753-3.html
   My bibliography  Save this article

Generalized Farkas Lemma with Adjustable Variables and Two-Stage Robust Linear Programs

Author

Listed:
  • Thai Doan Chuong

    (Ton Duc Thang University
    Ton Duc Thang University)

  • Vaithilingam Jeyakumar

    (University of New South Wales)

Abstract

In this paper, we establish strong duality between affinely adjustable two-stage robust linear programs and their dual semidefinite programs under a general uncertainty set, that covers most of the commonly used uncertainty sets of robust optimization. This is achieved by first deriving a new version of Farkas’ lemma for a parametric linear inequality system with affinely adjustable variables. Our strong duality theorem not only shows that the primal and dual program values are equal, but also allows one to find the value of a two-stage robust linear program by solving a semidefinite linear program. In the case of an ellipsoidal uncertainty set, it yields a corresponding strong duality result with a second-order cone program as its dual. To illustrate the efficacy of our results, we show how optimal storage cost of an adjustable two-stage lot-sizing problem under a ball uncertainty set can be found by solving its dual semidefinite program, using a commonly available software.

Suggested Citation

  • Thai Doan Chuong & Vaithilingam Jeyakumar, 2020. "Generalized Farkas Lemma with Adjustable Variables and Two-Stage Robust Linear Programs," Journal of Optimization Theory and Applications, Springer, vol. 187(2), pages 488-519, November.
    • Handle: RePEc:spr:joptap:v:187:y:2020:i:2:d:10.1007_s10957-020-01753-3
    DOI: 10.1007/s10957-020-01753-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-020-01753-3
    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/s10957-020-01753-3?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. V. Jeyakumar & J. Vicente-Pérez, 2014. "Dual Semidefinite Programs Without Duality Gaps for a Class of Convex Minimax Programs," Journal of Optimization Theory and Applications, Springer, vol. 162(3), pages 735-753, September.
    2. N. Dinh & V. Jeyakumar, 2014. "Rejoinder on: Farkas’ lemma: three decades of generalizations for mathematical optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 41-44, April.
    3. Xin Chen & Yuhan Zhang, 2009. "Uncertain Linear Programs: Extended Affinely Adjustable Robust Counterparts," Operations Research, INFORMS, vol. 57(6), pages 1469-1482, December.
    4. Yanıkoğlu, İhsan & Gorissen, Bram L. & den Hertog, Dick, 2019. "A survey of adjustable robust optimization," European Journal of Operational Research, Elsevier, vol. 277(3), pages 799-813.
    5. N. Dinh & V. Jeyakumar, 2014. "Farkas’ lemma: three decades of generalizations for mathematical optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 1-22, April.
    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. T. D. Chuong & V. Jeyakumar & G. Li & D. Woolnough, 2021. "Exact SDP reformulations of adjustable robust linear programs with box uncertainties under separable quadratic decision rules via SOS representations of non-negativity," Journal of Global Optimization, Springer, vol. 81(4), pages 1095-1117, December.

    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. Rahal, Said & Papageorgiou, Dimitri J. & Li, Zukui, 2021. "Hybrid strategies using linear and piecewise-linear decision rules for multistage adaptive linear optimization," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1014-1030.
    2. P. Montiel López & M. Ruiz Galán, 2016. "Nonlinear Programming via König’s Maximum Theorem," Journal of Optimization Theory and Applications, Springer, vol. 170(3), pages 838-852, September.
    3. N. Dinh & M. A. Goberna & M. A. López & T. H. Mo, 2017. "Farkas-Type Results for Vector-Valued Functions with Applications," Journal of Optimization Theory and Applications, Springer, vol. 173(2), pages 357-390, May.
    4. Ning Zhang & Chang Fang, 2020. "Saddle point approximation approaches for two-stage robust optimization problems," Journal of Global Optimization, Springer, vol. 78(4), pages 651-670, December.
    5. Angelos Georghiou & Angelos Tsoukalas & Wolfram Wiesemann, 2020. "A Primal–Dual Lifting Scheme for Two-Stage Robust Optimization," Operations Research, INFORMS, vol. 68(2), pages 572-590, March.
    6. Nguyen Dinh & Miguel A. Goberna & Dang H. Long & Marco A. López-Cerdá, 2019. "New Farkas-Type Results for Vector-Valued Functions: A Non-abstract Approach," Journal of Optimization Theory and Applications, Springer, vol. 182(1), pages 4-29, July.
    7. Filipe Rodrigues & Agostinho Agra & Cristina Requejo & Erick Delage, 2021. "Lagrangian Duality for Robust Problems with Decomposable Functions: The Case of a Robust Inventory Problem," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 685-705, May.
    8. Meijia Yang & Shu Wang & Yong Xia, 2022. "Toward Nonquadratic S-Lemma: New Theory and Application in Nonconvex Optimization," Journal of Optimization Theory and Applications, Springer, vol. 194(1), pages 353-363, July.
    9. Han, Biao & Shang, Chao & Huang, Dexian, 2021. "Multiple kernel learning-aided robust optimization: Learning algorithm, computational tractability, and usage in multi-stage decision-making," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1004-1018.
    10. Cambier, Adrien & Chardy, Matthieu & Figueiredo, Rosa & Ouorou, Adam & Poss, Michael, 2022. "Optimizing subscriber migrations for a telecommunication operator in uncertain context," European Journal of Operational Research, Elsevier, vol. 298(1), pages 308-321.
    11. 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).
    12. Da Li & Shijie Zhang & Yunhan Xiao, 2020. "Interval Optimization-Based Optimal Design of Distributed Energy Resource Systems under Uncertainties," Energies, MDPI, vol. 13(13), pages 1-18, July.
    13. Aras Selvi & Aharon Ben-Tal & Ruud Brekelmans & Dick den Hertog, 2022. "Convex Maximization via Adjustable Robust Optimization," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2091-2105, July.
    14. Shi, Ruifeng & Li, Shaopeng & Zhang, Penghui & Lee, Kwang Y., 2020. "Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization," Renewable Energy, Elsevier, vol. 153(C), pages 1067-1080.
    15. Walid Ben-Ameur & Adam Ouorou & Guanglei Wang & Mateusz Żotkiewicz, 2018. "Multipolar robust optimization," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 395-434, December.
    16. ten Eikelder, Stefan, 2021. "Biologically-based radiation therapy planning and adjustable robust optimization," Other publications TiSEM 2654a17b-0a3c-4006-b644-e, Tilburg University, School of Economics and Management.
    17. Aliakbari Sani, Sajad & Bahn, Olivier & Delage, Erick, 2022. "Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning," European Journal of Operational Research, Elsevier, vol. 303(1), pages 438-455.
    18. Vaithilingam Jeyakumar & Gue Myung Lee & Jae Hyoung Lee & Yingkun Huang, 2024. "Sum-of-Squares Relaxations in Robust DC Optimization and Feature Selection," Journal of Optimization Theory and Applications, Springer, vol. 200(1), pages 308-343, January.
    19. Angelos Georghiou & Wolfram Wiesemann & Daniel Kuhn, 2010. "Generalized Decision Rule Approximations for Stochastic Programming via Liftings," Working Papers 043, COMISEF.
    20. Fränk Plein & Johannes Thürauf & Martine Labbé & Martin Schmidt, 2022. "A bilevel optimization approach to decide the feasibility of bookings in the European gas market," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 95(3), pages 409-449, June.

    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:joptap:v:187:y:2020:i:2:d:10.1007_s10957-020-01753-3. 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.