IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v134y2005i1p201-21410.1007-s10479-005-5731-0.html
   My bibliography  Save this article

On the Convergence of the Cross-Entropy Method

Author

Listed:
  • L. Margolin

Abstract

The cross-entropy method is a relatively new method for combinatorial optimization. The idea of this method came from the simulation field and then was successfully applied to different combinatorial optimization problems. The method consists of an iterative stochastic procedure that makes use of the importance sampling technique. In this paper we prove the asymptotical convergence of some modifications of the cross-entropy method. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • L. Margolin, 2005. "On the Convergence of the Cross-Entropy Method," Annals of Operations Research, Springer, vol. 134(1), pages 201-214, February.
  • Handle: RePEc:spr:annopr:v:134:y:2005:i:1:p:201-214:10.1007/s10479-005-5731-0
    DOI: 10.1007/s10479-005-5731-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-005-5731-0
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10479-005-5731-0?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. Reuven Rubinstein, 1999. "The Cross-Entropy Method for Combinatorial and Continuous Optimization," Methodology and Computing in Applied Probability, Springer, vol. 1(2), pages 127-190, September.
    2. Rubinstein, Reuven Y., 1997. "Optimization of computer simulation models with rare events," European Journal of Operational Research, Elsevier, vol. 99(1), pages 89-112, May.
    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. Fangyuan Zhang & Jie Ding & Shili Lin, 2017. "Testing for Associations of Opposite Directionality in a Heterogeneous Population," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 137-159, June.
    2. Nguyen, Hoa T.M. & Chow, Andy H.F. & Ying, Cheng-shuo, 2021. "Pareto routing and scheduling of dynamic urban rail transit services with multi-objective cross entropy method," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    3. Ali Eshragh & Jerzy Filar & Michael Haythorpe, 2011. "A hybrid simulation-optimization algorithm for the Hamiltonian cycle problem," Annals of Operations Research, Springer, vol. 189(1), pages 103-125, September.
    4. Shili Lin & Jie Ding, 2009. "Integration of Ranked Lists via Cross Entropy Monte Carlo with Applications to mRNA and microRNA Studies," Biometrics, The International Biometric Society, vol. 65(1), pages 9-18, March.
    5. Dirk P. Kroese & Sergey Porotsky & Reuven Y. Rubinstein, 2006. "The Cross-Entropy Method for Continuous Multi-Extremal Optimization," Methodology and Computing in Applied Probability, Springer, vol. 8(3), pages 383-407, September.
    6. Zhengsong Lin & Yuting Wang & Xinyue Ye & Yuxi Wan & Tianjun Lu & Yu Han, 2022. "Effects of Low-Carbon Visualizations in Landscape Design Based on Virtual Eye-Movement Behavior Preference," Land, MDPI, vol. 11(6), pages 1-17, 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. K.-P. Hui & N. Bean & M. Kraetzl & Dirk Kroese, 2005. "The Cross-Entropy Method for Network Reliability Estimation," Annals of Operations Research, Springer, vol. 134(1), pages 101-118, February.
    2. Fahimnia, Behnam & Sarkis, Joseph & Eshragh, Ali, 2015. "A tradeoff model for green supply chain planning:A leanness-versus-greenness analysis," Omega, Elsevier, vol. 54(C), pages 173-190.
    3. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022. "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
    4. Ali Eshragh & Jerzy Filar & Michael Haythorpe, 2011. "A hybrid simulation-optimization algorithm for the Hamiltonian cycle problem," Annals of Operations Research, Springer, vol. 189(1), pages 103-125, September.
    5. Qun Niu & Ming You & Zhile Yang & Yang Zhang, 2021. "Economic Emission Dispatch Considering Renewable Energy Resources—A Multi-Objective Cross Entropy Optimization Approach," Sustainability, MDPI, vol. 13(10), pages 1-33, May.
    6. J Morio & R Pastel, 2012. "Plug-in estimation of d-dimensional density minimum volume set of a rare event in a complex system," Journal of Risk and Reliability, , vol. 226(3), pages 337-345, June.
    7. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    8. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    9. Benham, Tim & Duan, Qibin & Kroese, Dirk P. & Liquet, Benoît, 2017. "CEoptim: Cross-Entropy R Package for Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i08).
    10. Mattrand, C. & Bourinet, J.-M., 2014. "The cross-entropy method for reliability assessment of cracked structures subjected to random Markovian loads," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 171-182.
    11. Ad Ridder, 2004. "Importance Sampling Simulations of Markovian Reliability Systems using Cross Entropy," Tinbergen Institute Discussion Papers 04-018/4, Tinbergen Institute.
    12. Masoud Esmaeilikia & Behnam Fahimnia & Joeseph Sarkis & Kannan Govindan & Arun Kumar & John Mo, 2016. "A tactical supply chain planning model with multiple flexibility options: an empirical evaluation," Annals of Operations Research, Springer, vol. 244(2), pages 429-454, September.
    13. Fahimnia, Behnam & Sarkis, Joseph & Choudhary, Alok & Eshragh, Ali, 2015. "Tactical supply chain planning under a carbon tax policy scheme: A case study," International Journal of Production Economics, Elsevier, vol. 164(C), pages 206-215.
    14. Joshua Chan & Eric Eisenstat & Xuewen Yu, 2022. "Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis," Papers 2207.03988, arXiv.org.
    15. Morio, Jérôme, 2011. "Non-parametric adaptive importance sampling for the probability estimation of a launcher impact position," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 178-183.
    16. Sze Him Leung & Ji Meng Loh & Chun Yip Yau & Zhengyuan Zhu, 2021. "Spatial Sampling Design Using Generalized Neyman–Scott Process," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(1), pages 105-127, March.
    17. Nguyen, Hoa T.M. & Chow, Andy H.F. & Ying, Cheng-shuo, 2021. "Pareto routing and scheduling of dynamic urban rail transit services with multi-objective cross entropy method," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    18. Shi Yang & Shi Weiping & Wang Mengqiao & Lee Ji-Hyun & Kang Huining & Jiang Hui, 2023. "Accurate and fast small p-value estimation for permutation tests in high-throughput genomic data analysis with the cross-entropy method," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 22(1), pages 1-22, January.
    19. Hao Su & Qun Niu & Zhile Yang, 2023. "Optimal Power Flow Using Improved Cross-Entropy Method," Energies, MDPI, vol. 16(14), pages 1-33, July.
    20. Ferdinand Bollwein & Stephan Westphal, 2022. "Oblique decision tree induction by cross-entropy optimization based on the von Mises–Fisher distribution," Computational Statistics, Springer, vol. 37(5), pages 2203-2229, November.

    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:134:y:2005:i:1:p:201-214:10.1007/s10479-005-5731-0. 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.