IDEAS home Printed from https://ideas.repec.org/p/tiu/tiutis/3d774c6d-8141-4f31-a621-5f0d868684f2.html
   My bibliography  Save this paper

Complexity analysis of a sampling-based interior point method for convex optimization

Author

Listed:
  • Badenbroek, Riley

    (Tilburg University, School of Economics and Management)

  • de Klerk, Etienne

    (Tilburg University, School of Economics and Management)

Abstract

No abstract is available for this item.

Suggested Citation

  • Badenbroek, Riley & de Klerk, Etienne, 2022. "Complexity analysis of a sampling-based interior point method for convex optimization," Other publications TiSEM 3d774c6d-8141-4f31-a621-5, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:3d774c6d-8141-4f31-a621-5f0d868684f2
    as

    Download full text from publisher

    File URL: https://pure.uvt.nl/ws/portalfiles/portal/46393218/Sampling_IPM_MOR_v2.1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Robert L. Smith, 1984. "Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions," Operations Research, INFORMS, vol. 32(6), pages 1296-1308, December.
    2. David E. Kaufman & Robert L. Smith, 1998. "Direction Choice for Accelerated Convergence in Hit-and-Run Sampling," Operations Research, INFORMS, vol. 46(1), pages 84-95, February.
    3. Claude J. P. Bélisle & H. Edwin Romeijn & Robert L. Smith, 1993. "Hit-and-Run Algorithms for Generating Multivariate Distributions," Mathematics of Operations Research, INFORMS, vol. 18(2), pages 255-266, May.
    4. Adam Tauman Kalai & Santosh Vempala, 2006. "Simulated Annealing for Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 31(2), pages 253-266, 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. Riley Badenbroek & Etienne Klerk, 2022. "Simulated Annealing for Convex Optimization: Rigorous Complexity Analysis and Practical Perspectives," Journal of Optimization Theory and Applications, Springer, vol. 194(2), pages 465-491, August.

    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. Cyril Bachelard & Apostolos Chalkis & Vissarion Fisikopoulos & Elias Tsigaridas, 2024. "Randomized Control in Performance Analysis and Empirical Asset Pricing," Papers 2403.00009, arXiv.org.
    2. Stephen Baumert & Archis Ghate & Seksan Kiatsupaibul & Yanfang Shen & Robert L. Smith & Zelda B. Zabinsky, 2009. "Discrete Hit-and-Run for Sampling Points from Arbitrary Distributions Over Subsets of Integer Hyperrectangles," Operations Research, INFORMS, vol. 57(3), pages 727-739, June.
    3. Riley Badenbroek & Etienne Klerk, 2022. "Simulated Annealing for Convex Optimization: Rigorous Complexity Analysis and Practical Perspectives," Journal of Optimization Theory and Applications, Springer, vol. 194(2), pages 465-491, August.
    4. de Klerk, Etienne & Badenbroek, Riley, 2022. "Simulated annealing with hit-and-run for convex optimization: complexity analysis and practical perspectives," Other publications TiSEM 323b4588-65e0-4889-a555-9, Tilburg University, School of Economics and Management.
    5. Huseyin Mete & Yanfang Shen & Zelda Zabinsky & Seksan Kiatsupaibul & Robert Smith, 2011. "Pattern discrete and mixed Hit-and-Run for global optimization," Journal of Global Optimization, Springer, vol. 50(4), pages 597-627, August.
    6. Luis V. Montiel & J. Eric Bickel, 2014. "A Generalized Sampling Approach for Multilinear Utility Functions Given Partial Preference Information," Decision Analysis, INFORMS, vol. 11(3), pages 147-170, September.
    7. Etienne de Klerk & Monique Laurent, 2018. "Comparison of Lasserre’s Measure-Based Bounds for Polynomial Optimization to Bounds Obtained by Simulated Annealing," Mathematics of Operations Research, INFORMS, vol. 43(4), pages 1317-1325, November.
    8. Shirin Fallahi & Hans J Skaug & Guttorm Alendal, 2020. "A comparison of Monte Carlo sampling methods for metabolic network models," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-24, July.
    9. Sorawit Saengkyongam & Anthony Hayter & Seksan Kiatsupaibul & Wei Liu, 2020. "Efficient computation of the stochastic behavior of partial sum processes," Computational Statistics, Springer, vol. 35(1), pages 343-358, March.
    10. Cyril Bachelard & Apostolos Chalkis & Vissarion Fisikopoulos & Elias Tsigaridas, 2023. "Randomized geometric tools for anomaly detection in stock markets," Post-Print hal-04223511, HAL.
    11. de Klerk, Etienne & Laurent, Monique, 2017. "Comparison of Lasserre's Measure-based Bounds for Polynomial Optimization to Bounds Obtained by Simulated Annealing," Other publications TiSEM 7a865ba0-bffb-43fb-a376-7, Tilburg University, School of Economics and Management.
    12. Boris Polyak & Elena Gryazina, 2011. "Randomized methods based on new Monte Carlo schemes for control and optimization," Annals of Operations Research, Springer, vol. 189(1), pages 343-356, September.
    13. Cyril Bachelard & Apostolos Chalkis & Vissarion Fisikopoulos & Elias Tsigaridas, 2022. "Randomized geometric tools for anomaly detection in stock markets," Papers 2205.03852, arXiv.org, revised May 2022.
    14. Bélisle, Claude, 2000. "Slow hit-and-run sampling," Statistics & Probability Letters, Elsevier, vol. 47(1), pages 33-43, March.
    15. de Klerk, Etienne & Laurent, Monique, 2018. "Comparison of Lasserre's measure-based bounds for polynomial optimization to bounds obtained by simulated annealing," Other publications TiSEM 78f8f496-dc89-413e-864d-f, Tilburg University, School of Economics and Management.
    16. Nabil Kahalé, 2019. "Efficient Simulation of High Dimensional Gaussian Vectors," Mathematics of Operations Research, INFORMS, vol. 44(1), pages 58-73, February.
    17. Luis V. Montiel & J. Eric Bickel, 2013. "Approximating Joint Probability Distributions Given Partial Information," Decision Analysis, INFORMS, vol. 10(1), pages 26-41, March.
    18. Huseyin Onur Mete & Zelda B. Zabinsky, 2014. "Multiobjective Interacting Particle Algorithm for Global Optimization," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 500-513, August.
    19. Richard J. Caron & Tim Traynor & Shafiu Jibrin, 2010. "Feasibility and Constraint Analysis of Sets of Linear Matrix Inequalities," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 144-153, February.
    20. Tsionas, Mike G., 2020. "A coherent approach to Bayesian Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 281(2), pages 439-448.

    More about this item

    Statistics

    Access and download statistics

    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:tiu:tiutis:3d774c6d-8141-4f31-a621-5f0d868684f2. 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: Richard Broekman (email available below). General contact details of provider: https://www.tilburguniversity.edu/about/schools/economics-and-management/ .

    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.