IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v119y2018icp99-117.html
   My bibliography  Save this article

An algorithm based on semidefinite programming for finding minimax optimal designs

Author

Listed:
  • Duarte, Belmiro P.M.
  • Sagnol, Guillaume
  • Wong, Weng Kee

Abstract

An algorithm based on a delayed constraint generation method for solving semi-infinite programs for constructing minimax optimal designs for nonlinear models is proposed. The outer optimization level of the minimax optimization problem is solved using a semidefinite programming based approach that requires the design space be discretized. A nonlinear programming solver is then used to solve the inner program to determine the combination of the parameters that yields the worst-case value of the design criterion. The proposed algorithm is applied to find minimax optimal designs for the logistic model, the flexible 4-parameter Hill homoscedastic model and the general nth order consecutive reaction model, and shows that it (i) produces designs that compare well with minimax D−optimal designs obtained from semi-infinite programming method in the literature; (ii) can be applied to semidefinite representable optimality criteria, that include the common A−,E−,G−,I− and D-optimality criteria; (iii) can tackle design problems with arbitrary linear constraints on the weights; and (iv) is fast and relatively easy to use.

Suggested Citation

  • Duarte, Belmiro P.M. & Sagnol, Guillaume & Wong, Weng Kee, 2018. "An algorithm based on semidefinite programming for finding minimax optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 99-117.
  • Handle: RePEc:eee:csdana:v:119:y:2018:i:c:p:99-117
    DOI: 10.1016/j.csda.2017.09.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947317302086
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2017.09.008?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. Arne Stolbjerg Drud, 1994. "CONOPT—A Large-Scale GRG Code," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 207-216, May.
    2. Lopez, Marco & Still, Georg, 2007. "Semi-infinite programming," European Journal of Operational Research, Elsevier, vol. 180(2), pages 491-518, July.
    3. Masoudi, Ehsan & Holling, Heinz & Wong, Weng Kee, 2017. "Application of imperialist competitive algorithm to find minimax and standardized maximin optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 330-345.
    4. Alexander Mitsos & Angelos Tsoukalas, 2015. "Global optimization of generalized semi-infinite programs via restriction of the right hand side," Journal of Global Optimization, Springer, vol. 61(1), pages 1-17, January.
    5. Stanislav Žaković & Berc Rustem, 2003. "Semi-Infinite Programming and Applications to Minimax Problems," Annals of Operations Research, Springer, vol. 124(1), pages 81-110, November.
    6. Martin-Martin, R. & Torsney, B. & Lopez-Fidalgo, J., 2007. "Construction of marginally and conditionally restricted designs using multiplicative algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5547-5561, August.
    7. Belmiro P. M. Duarte & Weng Kee Wong, 2015. "Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming-Based Approach," International Statistical Review, International Statistical Institute, vol. 83(2), pages 239-262, August.
    8. Duarte, Belmiro P.M. & Wong, Weng Kee & Atkinson, Anthony C., 2015. "A Semi-Infinite Programming based algorithm for determining T-optimum designs for model discrimination," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 11-24.
    9. Aharon Ben-Tal & Arkadi Nemirovski, 2001. "On Polyhedral Approximations of the Second-Order Cone," Mathematics of Operations Research, INFORMS, vol. 26(2), pages 193-205, May.
    10. Harman, Radoslav & Jurík, Tomás, 2008. "Computing c-optimal experimental designs using the simplex method of linear programming," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 247-254, 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. Víctor Casero-Alonso & Andrey Pepelyshev & Weng K. Wong, 2018. "A web-based tool for designing experimental studies to detect hormesis and estimate the threshold dose," Statistical Papers, Springer, vol. 59(4), pages 1307-1324, December.
    2. Sahu, Nitesh & Babu, Prabhu, 2021. "A new monotonic algorithm for the E-optimal experiment design problem," Statistics & Probability Letters, Elsevier, vol. 174(C).

    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. Belmiro P. M. Duarte, 2023. "Exact Optimal Designs of Experiments for Factorial Models via Mixed-Integer Semidefinite Programming," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
    2. Duarte, Belmiro P.M. & Atkinson, Anthony C. & Granjo, Jose F.O & Oliveira, Nuno M.C, 2022. "Optimal design of experiments for implicit models," LSE Research Online Documents on Economics 107584, London School of Economics and Political Science, LSE Library.
    3. Ni, Yuanming & Steinshamn, Stein I. & Kvamsdal, Sturla F., 2022. "Negative shocks in an age-structured bioeconomic model and how to deal with them," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 15-30.
    4. Huiyi Cao & Kamil A. Khan, 2023. "General convex relaxations of implicit functions and inverse functions," Journal of Global Optimization, Springer, vol. 86(3), pages 545-572, July.
    5. Artur M. Schweidtmann & Alexander Mitsos, 2019. "Deterministic Global Optimization with Artificial Neural Networks Embedded," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 925-948, March.
    6. Santos, Lucas F. & Costa, Caliane B.B. & Caballero, José A. & Ravagnani, Mauro A.S.S., 2022. "Framework for embedding black-box simulation into mathematical programming via kriging surrogate model applied to natural gas liquefaction process optimization," Applied Energy, Elsevier, vol. 310(C).
    7. Durand-Lasserve, Olivier & Almutairi, Hossa & Aljarboua, Abdullah & Pierru, Axel & Pradhan, Shreekar & Murphy, Frederic, 2023. "Hard-linking a top-down economic model with a bottom-up energy system for an oil-exporting country with price controls," Energy, Elsevier, vol. 266(C).
    8. Jan Schwientek & Tobias Seidel & Karl-Heinz Küfer, 2021. "A transformation-based discretization method for solving general semi-infinite optimization problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 93(1), pages 83-114, February.
    9. Emmanuel Ogbe & Xiang Li, 2019. "A joint decomposition method for global optimization of multiscenario nonconvex mixed-integer nonlinear programs," Journal of Global Optimization, Springer, vol. 75(3), pages 595-629, November.
    10. Marian Leimbach & Anselm Schultes & Lavinia Baumstark & Anastasis Giannousakis & Gunnar Luderer, 2017. "Solution algorithms for regional interactions in large-scale integrated assessment models of climate change," Annals of Operations Research, Springer, vol. 255(1), pages 29-45, August.
    11. Mohammad R. Oskoorouchi & Hamid R. Ghaffari & Tamás Terlaky & Dionne M. Aleman, 2011. "An Interior Point Constraint Generation Algorithm for Semi-Infinite Optimization with Health-Care Application," Operations Research, INFORMS, vol. 59(5), pages 1184-1197, October.
    12. Xu, Jianwei & Liang, Yingzong & Luo, Xianglong & Chen, Jianyong & Yang, Zhi & Chen, Ying, 2023. "Techno-economic-environmental analysis of direct-contact membrane distillation systems integrated with low-grade heat sources: A multi-objective optimization approach," Applied Energy, Elsevier, vol. 349(C).
    13. Victor Reyes & Ignacio Araya, 2021. "AbsTaylor: upper bounding with inner regions in nonlinear continuous global optimization problems," Journal of Global Optimization, Springer, vol. 79(2), pages 413-429, February.
    14. Fuentes-Cortés, Luis Fabián & Flores-Tlacuahuac, Antonio, 2018. "Integration of distributed generation technologies on sustainable buildings," Applied Energy, Elsevier, vol. 224(C), pages 582-601.
    15. Cignitti, Stefano & Andreasen, Jesper G. & Haglind, Fredrik & Woodley, John M. & Abildskov, Jens, 2017. "Integrated working fluid-thermodynamic cycle design of organic Rankine cycle power systems for waste heat recovery," Applied Energy, Elsevier, vol. 203(C), pages 442-453.
    16. Jianhui Xie & Qiwei Xie & Yongjun Li & Liang Liang, 2021. "Solving data envelopment analysis models with sum-of-fractional objectives: a global optimal approach based on the multiparametric disaggregation technique," Annals of Operations Research, Springer, vol. 304(1), pages 453-480, September.
    17. Gao, Lei & Hwang, Yunho & Cao, Tao, 2019. "An overview of optimization technologies applied in combined cooling, heating and power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    18. Ibrić, Nidret & Ahmetović, Elvis & Kravanja, Zdravko & Maréchal, François & Kermani, Maziar, 2017. "Simultaneous synthesis of non-isothermal water networks integrated with process streams," Energy, Elsevier, vol. 141(C), pages 2587-2612.
    19. Chernonog, Tatyana & Goldberg, Noam, 2018. "On the multi-product newsvendor with bounded demand distributions," International Journal of Production Economics, Elsevier, vol. 203(C), pages 38-47.
    20. Klaus Mittenzwei, 2020. "Greenhouse Gas Emissions in Norwegian Agriculture: The Regional and Structural Dimension," Sustainability, MDPI, vol. 12(6), pages 1-13, March.

    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:eee:csdana:v:119:y:2018:i:c:p:99-117. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.