IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v86y2023i1d10.1007_s10589-023-00483-2.html
   My bibliography  Save this article

Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction

Author

Listed:
  • Albert S. Berahas

    (University of Michigan)

  • Jiahao Shi

    (University of Michigan)

  • Zihong Yi

    (University of Michigan)

  • Baoyu Zhou

    (University of Chicago)

Abstract

In this paper, we propose a stochastic method for solving equality constrained optimization problems that utilizes predictive variance reduction. Specifically, we develop a method based on the sequential quadratic programming paradigm that employs variance reduction in the gradient approximations. Under reasonable assumptions, we prove that a measure of first-order stationarity evaluated at the iterates generated by our proposed algorithm converges to zero in expectation from arbitrary starting points, for both constant and adaptive step size strategies. Finally, we demonstrate the practical performance of our proposed algorithm on constrained binary classification problems that arise in machine learning.

Suggested Citation

  • Albert S. Berahas & Jiahao Shi & Zihong Yi & Baoyu Zhou, 2023. "Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction," Computational Optimization and Applications, Springer, vol. 86(1), pages 79-116, September.
  • Handle: RePEc:spr:coopap:v:86:y:2023:i:1:d:10.1007_s10589-023-00483-2
    DOI: 10.1007/s10589-023-00483-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10589-023-00483-2
    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/s10589-023-00483-2?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. Nilanjan Chatterjee & Yi-Hau Chen & Paige Maas & Raymond J. Carroll, 2016. "Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-Level Information From External Big Data Sources," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 107-117, March.
    2. Jianchao Bai & William W. Hager & Hongchao Zhang, 2022. "An inexact accelerated stochastic ADMM for separable convex optimization," Computational Optimization and Applications, Springer, vol. 81(2), pages 479-518, March.
    Full references (including those not matched with items on IDEAS)

    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. Debashis Ghosh & Michael S. Sabel, 2022. "A Weighted Sample Framework to Incorporate External Calculators for Risk Modeling," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 363-379, December.
    2. Cao, Yongxiu & Yu, Jichang, 2023. "Adjusting for unmeasured confounding in survival causal effect using validation data," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    3. Jie He & Hui Li & Shumei Zhang & Xiaogang Duan, 2019. "Additive hazards model with auxiliary subgroup survival information," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 128-149, January.
    4. Chixiang Chen & Ming Wang & Shuo Chen, 2023. "An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis," Biometrics, The International Biometric Society, vol. 79(4), pages 2947-2960, December.
    5. Bo Han & Ingrid Van Keilegom & Xiaoguang Wang, 2022. "Semiparametric estimation of the nonmixture cure model with auxiliary survival information," Biometrics, The International Biometric Society, vol. 78(2), pages 448-459, June.
    6. Sahar Z. Zangeneh & Roderick J. Little, 2022. "Likelihood‐Based Inference for the Finite Population Mean with Post‐Stratification Information Under Non‐Ignorable Non‐Response," International Statistical Review, International Statistical Institute, vol. 90(S1), pages 17-36, December.
    7. Ruoyu Wang & Qihua Wang & Wang Miao, 2023. "A robust fusion-extraction procedure with summary statistics in the presence of biased sources," Biometrika, Biometrika Trust, vol. 110(4), pages 1023-1040.
    8. Jan Pablo Burgard & Joscha Krause & Simon Schmaus, 2019. "Estimation of Regional Transition Probabilities for Spatial Dynamic Microsimulations from Survey Data Lacking in Regional Detail," Research Papers in Economics 2019-12, University of Trier, Department of Economics.
    9. Fei Gao & K. C. G. Chan, 2023. "Noniterative adjustment to regression estimators with population‐based auxiliary information for semiparametric models," Biometrics, The International Biometric Society, vol. 79(1), pages 140-150, March.
    10. Tian Gu & Jeremy Michael George Taylor & Bhramar Mukherjee, 2023. "A synthetic data integration framework to leverage external summary‐level information from heterogeneous populations," Biometrics, The International Biometric Society, vol. 79(4), pages 3831-3845, December.
    11. Han Zhang & Lu Deng & William Wheeler & Jing Qin & Kai Yu, 2022. "Integrative analysis of multiple case‐control studies," Biometrics, The International Biometric Society, vol. 78(3), pages 1080-1091, September.
    12. Ying Sheng & Yifei Sun & Chiung‐Yu Huang & Mi‐Ok Kim, 2022. "Synthesizing external aggregated information in the presence of population heterogeneity: A penalized empirical likelihood approach," Biometrics, The International Biometric Society, vol. 78(2), pages 679-690, June.
    13. Takumi Saegusa, 2020. "Confidence bands for a distribution function with merged data from multiple sources," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 144-158, August.
    14. Ying Sheng & Yifei Sun & Detian Deng & Chiung‐Yu Huang, 2020. "Censored linear regression in the presence or absence of auxiliary survival information," Biometrics, The International Biometric Society, vol. 76(3), pages 734-745, September.
    15. Ziqi Chen & Jing Ning & Yu Shen & Jing Qin, 2021. "Combining primary cohort data with external aggregate information without assuming comparability," Biometrics, The International Biometric Society, vol. 77(3), pages 1024-1036, September.
    16. Prosenjit Kundu & Nilanjan Chatterjee, 2023. "Logistic regression analysis of two‐phase studies using generalized method of moments," Biometrics, The International Biometric Society, vol. 79(1), pages 241-252, March.
    17. Saegusa Takumi, 2020. "Confidence bands for a distribution function with merged data from multiple sources," Statistics in Transition New Series, Statistics Poland, vol. 21(4), pages 144-158, August.
    18. Burgard, Jan Pablo & Krause, Joscha & Schmaus, Simon, 2021. "Estimation of regional transition probabilities for spatial dynamic microsimulations from survey data lacking in regional detail," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    19. Yu‐Jen Cheng & Yen‐Chun Liu & Chang‐Yu Tsai & Chiung‐Yu Huang, 2023. "Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity," Biometrics, The International Biometric Society, vol. 79(3), pages 1996-2009, September.

    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:coopap:v:86:y:2023:i:1:d:10.1007_s10589-023-00483-2. 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.