IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v71y2018i2d10.1007_s10589-018-0017-z.html
   My bibliography  Save this article

Exact augmented Lagrangian functions for nonlinear semidefinite programming

Author

Listed:
  • Ellen H. Fukuda

    (Kyoto University)

  • Bruno F. Lourenço

    (University of Tokyo)

Abstract

In this paper, we study augmented Lagrangian functions for nonlinear semidefinite programming (NSDP) problems with exactness properties. The term exact is used in the sense that the penalty parameter can be taken appropriately, so a single minimization of the augmented Lagrangian recovers a solution of the original problem. This leads to reformulations of NSDP problems into unconstrained nonlinear programming ones. Here, we first establish a unified framework for constructing these exact functions, generalizing Di Pillo and Lucidi’s work from 1996, that was aimed at solving nonlinear programming problems. Then, through our framework, we propose a practical augmented Lagrangian function for NSDP, proving that it is continuously differentiable and exact under the so-called nondegeneracy condition. We also present some preliminary numerical experiments.

Suggested Citation

  • Ellen H. Fukuda & Bruno F. Lourenço, 2018. "Exact augmented Lagrangian functions for nonlinear semidefinite programming," Computational Optimization and Applications, Springer, vol. 71(2), pages 457-482, November.
  • Handle: RePEc:spr:coopap:v:71:y:2018:i:2:d:10.1007_s10589-018-0017-z
    DOI: 10.1007/s10589-018-0017-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10589-018-0017-z
    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/s10589-018-0017-z?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. Roberto Andreani & Ellen H. Fukuda & Paulo J. S. Silva, 2013. "A Gauss–Newton Approach for Solving Constrained Optimization Problems Using Differentiable Exact Penalties," Journal of Optimization Theory and Applications, Springer, vol. 156(2), pages 417-449, February.
    2. Florian Jarre, 2012. "Elementary Optimality Conditions for Nonlinear SDPs," International Series in Operations Research & Management Science, in: Miguel F. Anjos & Jean B. Lasserre (ed.), Handbook on Semidefinite, Conic and Polynomial Optimization, chapter 0, pages 455-470, Springer.
    3. Thiago André & Paulo Silva, 2010. "Exact penalties for variational inequalities with applications to nonlinear complementarity problems," Computational Optimization and Applications, Springer, vol. 47(3), pages 401-429, November.
    4. Hiroshi Konno & Naoya Kawadai & Dai Wu, 2003. "Estimation of failure probability using semi-definite logit model," Computational Management Science, Springer, vol. 1(1), pages 59-73, December.
    5. Alexander Shapiro & Jie Sun, 2004. "Some Properties of the Augmented Lagrangian in Cone Constrained Optimization," Mathematics of Operations Research, INFORMS, vol. 29(3), pages 479-491, August.
    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. Shun Arahata & Takayuki Okuno & Akiko Takeda, 2023. "Complexity analysis of interior-point methods for second-order stationary points of nonlinear semidefinite optimization problems," Computational Optimization and Applications, Springer, vol. 86(2), pages 555-598, November.
    2. Yi Zhang & Liwei Zhang & Yue Wu, 2014. "The augmented Lagrangian method for a type of inverse quadratic programming problems over second-order cones," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 45-79, April.
    3. M. V. Dolgopolik, 2018. "Augmented Lagrangian functions for cone constrained optimization: the existence of global saddle points and exact penalty property," Journal of Global Optimization, Springer, vol. 71(2), pages 237-296, June.
    4. M. V. Dolgopolik, 2018. "A Unified Approach to the Global Exactness of Penalty and Augmented Lagrangian Functions I: Parametric Exactness," Journal of Optimization Theory and Applications, Springer, vol. 176(3), pages 728-744, March.
    5. J. Sun & L. W. Zhang & Y. Wu, 2006. "Properties of the Augmented Lagrangian in Nonlinear Semidefinite Optimization," Journal of Optimization Theory and Applications, Springer, vol. 129(3), pages 437-456, June.
    6. M. V. Dolgopolik, 2018. "A Unified Approach to the Global Exactness of Penalty and Augmented Lagrangian Functions II: Extended Exactness," Journal of Optimization Theory and Applications, Springer, vol. 176(3), pages 745-762, March.
    7. H. Luo & X. Huang & J. Peng, 2012. "Generalized weak sharp minima in cone-constrained convex optimization with applications," Computational Optimization and Applications, Springer, vol. 53(3), pages 807-821, December.
    8. Sheng-Long Hu & Zheng-Hai Huang, 2011. "Alternating direction method for bi-quadratic programming," Journal of Global Optimization, Springer, vol. 51(3), pages 429-446, November.
    9. Stuart M. Harwood, 2021. "Analysis of the Alternating Direction Method of Multipliers for Nonconvex Problems," SN Operations Research Forum, Springer, vol. 2(1), pages 1-29, March.
    10. Nguyen T. V. Hang & Boris S. Mordukhovich & M. Ebrahim Sarabi, 2022. "Augmented Lagrangian method for second-order cone programs under second-order sufficiency," Journal of Global Optimization, Springer, vol. 82(1), pages 51-81, January.
    11. H. Luo & H. Wu & G. Chen, 2012. "On the convergence of augmented Lagrangian methods for nonlinear semidefinite programming," Journal of Global Optimization, Springer, vol. 54(3), pages 599-618, November.
    12. R. Andreani & E. H. Fukuda & G. Haeser & D. O. Santos & L. D. Secchin, 2021. "On the use of Jordan Algebras for improving global convergence of an Augmented Lagrangian method in nonlinear semidefinite programming," Computational Optimization and Applications, Springer, vol. 79(3), pages 633-648, July.
    13. R. S. Burachik & X. Q. Yang & Y. Y. Zhou, 2017. "Existence of Augmented Lagrange Multipliers for Semi-infinite Programming Problems," Journal of Optimization Theory and Applications, Springer, vol. 173(2), pages 471-503, May.
    14. Chao Kan & Wen Song, 2015. "Second-order conditions for existence of augmented Lagrange multipliers for eigenvalue composite optimization problems," Journal of Global Optimization, Springer, vol. 63(1), pages 77-97, September.
    15. Hezhi Luo & Huixian Wu & Jianzhen Liu, 2015. "On Saddle Points in Semidefinite Optimization via Separation Scheme," Journal of Optimization Theory and Applications, Springer, vol. 165(1), pages 113-150, April.
    16. Roberto Andreani & Ellen H. Fukuda & Paulo J. S. Silva, 2013. "A Gauss–Newton Approach for Solving Constrained Optimization Problems Using Differentiable Exact Penalties," Journal of Optimization Theory and Applications, Springer, vol. 156(2), pages 417-449, February.
    17. T. Son & D. Kim & N. Tam, 2012. "Weak stability and strong duality of a class of nonconvex infinite programs via augmented Lagrangian," Journal of Global Optimization, Springer, vol. 53(2), pages 165-184, June.
    18. Ng, Kenyon & Turlach, Berwin A. & Murray, Kevin, 2019. "A flexible sequential Monte Carlo algorithm for parametric constrained regression," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 13-26.
    19. Jinchuan Zhou & Jein-Shan Chen, 2015. "On the existence of saddle points for nonlinear second-order cone programming problems," Journal of Global Optimization, Springer, vol. 62(3), pages 459-480, July.
    20. Chao Kan & Wen Song, 2015. "Augmented Lagrangian Duality for Composite Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 165(3), pages 763-784, June.

    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:71:y:2018:i:2:d:10.1007_s10589-018-0017-z. 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.