Skip to main content

Testing Regularity on Linear Semidefinite Optimization Problems

  • Conference paper
Operational Research

Part of the book series: CIM Series in Mathematical Sciences ((CIMSMS,volume 4))

Abstract

This paper presents a study of regularity of Semidefinite Programming (SDP) problems. Current methods for SDP rely on assumptions of regularity such as constraint qualifications (CQ) and well-posedness. In the absence of regularity, the characterization of optimality may fail and the convergence of algorithms is not guaranteed. Therefore, it is important to have procedures that verify the regularity of a given problem before applying any (standard) SDP solver. We suggest a simple numerical procedure to test within a desired accuracy if a given SDP problem is regular in terms of the fulfilment of the Slater CQ. Our procedure is based on the recently proposed DIIS algorithm that determines the immobile index subspace for SDP. We use this algorithm in a framework of an interactive decision support system. Numerical results using SDP problems from the literature and instances from the SDPLIB suite are presented, and a comparative analysis with other results on regularity available in the literature is made.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Anjos, M.F., Lasserre, J.B. (eds.): Handbook of Semidefinite, Conic and Polynomial Optimization: Theory, Algorithms, Software and Applications. International Series in Operational Research and Management Science, vol.Ā 166. Springer, New York (2012)

    Google ScholarĀ 

  2. Bonnans, J.F., Shapiro, A.: Perturbation Analysis of Optimization Problems. Springer, New York (2000)

    BookĀ  MATHĀ  Google ScholarĀ 

  3. Borchers, B.: SDPLIB 1.2, a library of semidefinite programming test problems. Optim. Methods Softw. 11(1), 683ā€“690 (1999)

    Google ScholarĀ 

  4. Cheung, Y., Schurr, S., Wolkowicz, H.: Preprocessing and Reduction for Degenerate Semidefinite Programs. Research Report CORR 2011-02. http://www.optimization-online.org/DB_FILE/2011/02/2929.pdf (2013). Revised in January 2013

  5. Dontchev, A.L., Zolezzi, T.: Well-Posed Optimization Problems. Lecture Notes in Mathematics, vol.Ā 1543. Springer, Berlin (1993)

    Google ScholarĀ 

  6. Freund, R.M.: Complexity of an Algorithm for Finding an Approximate Solution of a Semi-Definite Program, with no Regularity Condition (1995). Technical Report OR 302-94, Op. Research Center, MIT, Revised in December 1995

    Google ScholarĀ 

  7. Freund, R.M., Sun, J.: Semidefinite Programming I: Introduction and minimization of polynomials, System Optimization. Available at http://www.myoops.org/cocw/mit/NR/rdonlyres/Sloan-School-of-Management/15-094Systems-Optimization--Models-and-ComputationSpring2002/1B59FD11-A822-4C80-9301-47B127500648/0/lecture22.pdf (2002)

  8. Freund, R.M., OrdĆ³Ć±ez, F., Toh, K.C.: Behavioral Measures and their Correlation with IPM Iteration Counts on Semi-Definite Programming Problems. Math. Program. 109(2), 445ā€“475 (2007). Springer, New York

    Google ScholarĀ 

  9. Fujisawa, K., Futakata, Y., Kojima, M., Matsuyama, S., Nakamura, S., Nakata, K., Yamashita, M.: SDPA-M (SemiDefinite Programming Algorithm in MATLAB) Userā€™s Manual-V6.2.0, Series B: OR Department of Mathematical and Computing Sciences (2005)

    Google ScholarĀ 

  10. GƤrtner, B., MatouÅ”ek, J.: Interior Point Methods, Approximation Algorithms and Semidefinite Programming. Available at http://www.ti.inf.ethz.ch/ew/courses/ApproxSDP09/ (2009)

  11. Helmberg, C.: Semidefinite Programming for Combinatorial Optimization. ZIB Report, Berlin (2000)

    Google ScholarĀ 

  12. HernĆ”ndez-JimĆ©nez, B., Rojas-Medar, M.A., Osuna-GĆ³mez, R., Beato-Moreno, A.: Generalized convexity in non-regular programming problems with inequality-type constraints. J. Math. Anal. Appl. 352(2), 604ā€“613 (2009)

    ArticleĀ  MATHĀ  MathSciNetĀ  Google ScholarĀ 

  13. Jansson, C., Chaykin, D., Keil, C.: Rigorous error bounds for the optimal value in semidefinite programming. SIAM J. Numer. Anal. archive 46(1), 180ā€“200 (2007)

    ArticleĀ  MathSciNetĀ  Google ScholarĀ 

  14. Klatte, D.: First order constraint qualifications. In: Floudas, C.A., Pardalos, P.M. (eds.) Encyclopedia of Optimization, 2nd edn, pp.Ā 1055ā€“1060. Springer, US (2009)

    ChapterĀ  Google ScholarĀ 

  15. Klerk, E. de: Aspects of Semidefinite Programming ā€“ Interior Point Algorithms and Selected Applications. Applied Optimization, vol.Ā 65. Kluwer, Boston (2004)

    Google ScholarĀ 

  16. Kojima, M.: Introduction to Semidefinite Programs (Semidefinite Programming and Its Application), Institute for Mathematical Sciences National University of Singapore (2006)

    Google ScholarĀ 

  17. Kolman, B., Beck, R.E.: Elementary Linear Programming with Applications, 2nd edn, Academic Press, San Diego (1995)

    MATHĀ  Google ScholarĀ 

  18. Konsulova, A.S., Revalski, J.P.: Constrained convex optimization problems ā€“ well-posedness and stability. Numer. Funct. Anal. Optim. 15(7ā€“8), 889ā€“907 (1994)

    ArticleĀ  MATHĀ  MathSciNetĀ  Google ScholarĀ 

  19. Kostyukova, O.I., Tchemisova, T.V.: Optimality criterion without constraint qualification for linear semidefinite problems. J. Math. Sci. 182(2), 126ā€“143 (2012)

    ArticleĀ  MATHĀ  MathSciNetĀ  Google ScholarĀ 

  20. Luo, Z., Sturm, J., Zhang, S.: Duality results for conic convex programming, Econometric Institute Report No. 9719/A (1997)

    Google ScholarĀ 

  21. Mitchell, J., Krishnan, K.: A unifying framework for several cutting plane methods for semidefinite programming, Technical Report, Department of Computational and Applied Mathematics, Rice University (2003)

    Google ScholarĀ 

  22. MorĆ©, J.J.: The Levenberg-Marquardt algorithm: implementation and theory. In: Watson, G.A. (ed.) Numerical Analysis. Lecture Notes in Mathematics, vol.Ā 630, pp.Ā 105ā€“116. Springer, Berlin/Heidelberg (1977)

    Google ScholarĀ 

  23. Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, New York (1999)

    BookĀ  MATHĀ  Google ScholarĀ 

  24. Pataki, G.: Bad semidefinite programs: they all look the same, Technical report, Department of Operations Research, University of North Carolina (2011)

    Google ScholarĀ 

  25. Pedregal, P.: Introduction to Optimization. Springer, New York (2004)

    BookĀ  MATHĀ  Google ScholarĀ 

  26. Polik, I.: Semidefinite programming Feasibility and duality. Available at http://imre.polik.net/wp-content/uploads/IE496/POLIK_IE496_04_duality.pdf (2009)

  27. Renegar, J.: Some perturbation-theory for linear-programming. Math. Program. 65(1), 73ā€“91 (1994)

    ArticleĀ  MATHĀ  MathSciNetĀ  Google ScholarĀ 

  28. Sturm, J., Zhang, S.: On sensitivity of central solutions in semidefinite programming. Math. Program. 90(2), 205ā€“227 (1998). Springer

    Google ScholarĀ 

  29. Tikhonov, A.N., Arsenin, V.Y.: Solutions of ill-posed problems. John Wiley and Sons, New York (1977)

    MATHĀ  Google ScholarĀ 

  30. Todd, M.J.: Semidefinite optimization. Acta Numer. 10, 515ā€“560 (2001). Cambridge University Press

    Google ScholarĀ 

  31. Vandenberghe, L., Boyd, S.: Semidefinite Programming. SIAM Rev. 38(1), 49ā€“95 (1996)

    ArticleĀ  MATHĀ  MathSciNetĀ  Google ScholarĀ 

  32. Wolkowicz, H.: Duality for semidefinite programming. In: Floudas, C.A., Pardalos, P.M. (eds.) Encyclopedia of Optimization, 2nd edn, pp.Ā 811ā€“814. Springer, US (2009)

    ChapterĀ  Google ScholarĀ 

  33. Wolkowicz, H., Saigal, R., Vandenberghe, L.: Handbook of Semidefinite Programming: Theory, Algorithms, and Applications. Kluwer, Boston (2000)

    BookĀ  Google ScholarĀ 

  34. Zhang, Y.: Semidefinite Programming, Lecture 2. Available at http://rutcor.rutgers.edu/~alizadeh/CLASSES/95sprSDP/NOTES/lecture2.ps (1995)

Download references

Acknowledgements

The author would like to thank the anonymous referee for the valuable comments that have helped to improve the paper. This work was supported by Portuguese funds through the CIDMA ā€“ Center for Research and Development in Mathematics and Applications (University of Aveiro), and the Portuguese Foundation for Science and Technology (ā€œFCT ā€“ FundaĆ§Ć£o para a CiĆŖncia e a Tecnologiaā€), within project UID/MAT/04106/2013.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to EloĆ­sa Macedo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

Ā© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Macedo, E. (2015). Testing Regularity on Linear Semidefinite Optimization Problems. In: Almeida, J., Oliveira, J., Pinto, A. (eds) Operational Research. CIM Series in Mathematical Sciences, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-20328-7_13

Download citation

Publish with us

Policies and ethics