IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v060i04.html
   My bibliography  Save this article

trustOptim: An R Package for Trust Region Optimization with Sparse Hessians

Author

Listed:
  • Braun, Michael

Abstract

Trust region algorithms are nonlinear optimization tools that tend to be stable and reliable when the objective function is non-concave, ill-conditioned, or exhibits regions that are nearly flat. Additionally, most freely-available optimization routines do not exploit the sparsity of the Hessian when such sparsity exists, as in log posterior densities of Bayesian hierarchical models. The trustOptim package for the R programming language addresses both of these issues. It is intended to be robust, scalable and efficient for a large class of nonlinear optimization problems that are often encountered in statistics, such as finding posterior modes. The user must supply the objective function, gradient and Hessian. However, when used in conjunction with the sparseHessianFD package, the user does not need to supply the exact sparse Hessian, as long as the sparsity structure is known in advance. For models with a large number of parameters, but for which most of the cross-partial derivatives are zero (i.e., the Hessian is sparse), trustOptim offers dramatic performance improvements over existing options, in terms of computational time and memory footprint.

Suggested Citation

  • Braun, Michael, 2014. "trustOptim: An R Package for Trust Region Optimization with Sparse Hessians," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i04).
  • Handle: RePEc:jss:jstsof:v:060:i04
    DOI: http://hdl.handle.net/10.18637/jss.v060.i04
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v060i04/v60i04.pdf
    Download Restriction: no

    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v060i04/trustOptim_0.8.4.2.tar.gz
    Download Restriction: no

    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v060i04/v60i04-replication.zip
    Download Restriction: no

    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v060.i04?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
    ---><---

    References listed on IDEAS

    as
    1. Bates, Douglas & Eddelbuettel, Dirk, 2013. "Fast and Elegant Numerical Linear Algebra Using the RcppEigen Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i05).
    2. Nash, John C. & Varadhan, Ravi, 2011. "Unifying Optimization Algorithms to Aid Software System Users: optimx for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i09).
    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. Marra, Giampiero & Wyszynski, Karol, 2016. "Semi-parametric copula sample selection models for count responses," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 110-129.
    2. Alex Stringer & Patrick Brown & Jamie Stafford, 2021. "Approximate Bayesian inference for case‐crossover models," Biometrics, The International Biometric Society, vol. 77(3), pages 785-795, September.
    3. Michael Braun & Paul Damien, 2016. "Scalable Rejection Sampling for Bayesian Hierarchical Models," Marketing Science, INFORMS, vol. 35(3), pages 427-444, May.
    4. Varadhan, Ravi, 2014. "Numerical Optimization in R: Beyond optim," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i01).

    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. Hancock, Joana & Vieira, Sara & Lima, Hipólito & Schmitt, Vanessa & Pereira, Jaconias & Rebelo, Rui & Girondot, Marc, 2019. "Overcoming field monitoring restraints in estimating marine turtle internesting period by modelling individual nesting behaviour using capture-mark-recapture data," Ecological Modelling, Elsevier, vol. 402(C), pages 76-84.
    2. Jack McDonnell & Thomas McKenna & Kathryn A. Yurkonis & Deirdre Hennessy & Rafael Andrade Moral & Caroline Brophy, 2023. "A Mixed Model for Assessing the Effect of Numerous Plant Species Interactions on Grassland Biodiversity and Ecosystem Function Relationships," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 1-19, March.
    3. Arzum Akkaş & Nachiketa Sahoo, 2020. "Reducing Product Expiration by Aligning Salesforce Incentives: A Data‐driven Approach," Production and Operations Management, Production and Operations Management Society, vol. 29(8), pages 1992-2009, August.
    4. Zong, Weiyan & Zhang, Junyi & Yang, Xiaoguang, 2023. "Building a life-course intertemporal discrete choice model to analyze migration biographies," Journal of choice modelling, Elsevier, vol. 47(C).
    5. Fatima-Zahra Jaouimaa & Daniel Dempsey & Suzanne Van Osch & Stephen Kinsella & Kevin Burke & Jason Wyse & James Sweeney, 2021. "An age-structured SEIR model for COVID-19 incidence in Dublin, Ireland with framework for evaluating health intervention cost," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-25, December.
    6. Pötscher, Benedikt M. & Preinerstorfer, David, 2023. "How Reliable Are Bootstrap-Based Heteroskedasticity Robust Tests?," Econometric Theory, Cambridge University Press, vol. 39(4), pages 789-847, August.
    7. Martinetti, Davide & Geniaux, Ghislain, 2017. "Approximate likelihood estimation of spatial probit models," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 30-45.
    8. Aaron T L Lun & Hervé Pagès & Mike L Smith, 2018. "beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-15, May.
    9. Michael Braun & Paul Damien, 2016. "Scalable Rejection Sampling for Bayesian Hierarchical Models," Marketing Science, INFORMS, vol. 35(3), pages 427-444, May.
    10. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    11. Fritsch, Markus & Pua, Andrew Adrian Yu & Schnurbus, Joachim, 2019. "Pdynmc - An R-package for estimating linear dynamic panel data models based on linear and nonlinear moment conditions," Passauer Diskussionspapiere, Betriebswirtschaftliche Reihe B-39-19, University of Passau, Faculty of Business and Economics.
    12. repec:rri:wpaper:201301 is not listed on IDEAS
    13. Gong Chen & Hartmut Fricke & Ostap Okhrin & Judith Rosenow, 2022. "Importance of Weather Conditions in a Flight Corridor," Stats, MDPI, vol. 5(1), pages 1-27, March.
    14. Sameh Abdulah & Yuxiao Li & Jian Cao & Hatem Ltaief & David E. Keyes & Marc G. Genton & Ying Sun, 2023. "Large‐scale environmental data science with ExaGeoStatR," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    15. Stavrakoudis, Athanassios & Panagiotou, Dimitrios, 2016. "Price dependence and asymmetric responses between coffee varieties," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 17(2), June.
    16. Marchese, Scott & Diao, Guoqing, 2018. "Joint regression analysis of mixed-type outcome data via efficient scores," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 156-170.
    17. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
    18. Michal Engelman & Christopher L. Seplaki & Ravi Varadhan, 2017. "A Quiescent Phase in Human Mortality? Exploring the Ages of Least Vulnerability," Demography, Springer;Population Association of America (PAA), vol. 54(3), pages 1097-1118, June.
    19. Baty, Florent & Ritz, Christian & Charles, Sandrine & Brutsche, Martin & Flandrois, Jean-Pierre & Delignette-Muller, Marie-Laure, 2015. "A Toolbox for Nonlinear Regression in R: The Package nlstools," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i05).
    20. Herrera, Rodrigo & Schipp, Bernhard, 2014. "Statistics of extreme events in risk management: The impact of the subprime and global financial crisis on the German stock market," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 218-238.
    21. Ghysels, Eric & Kvedaras, Virmantas & Zemlys, Vaidotas, 2016. "Mixed Frequency Data Sampling Regression Models: The R Package midasr," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i04).

    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:jss:jstsof:v:060:i04. 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: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    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.