Skip to main content
Log in

A logarithmic barrier interior-point method based on majorant functions for second-order cone programming

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

We present a logarithmic barrier interior-point method for solving a second-order cone programming problem. Newton’s method is used to compute the descent direction. The main contribution of this paper is that it uniquely uses the so-called majorant functions as an efficient alternative to line search methods to determine the displacement step along the direction while solving second-order cone programs. The efficiency of our method is shown by presenting numerical experiments.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Benterki, D., Crouzeix, J.P., Merikhi, B.: A numerical feasible interior point method for linear semidefinite programs. RAIRO Oper. Res. 41, 49–59 (2007)

    Article  MathSciNet  Google Scholar 

  2. Touil, I., Benterki, D., Yassine, A.: A feasible primal-dual interior point method for linear semidefinite programming. J. Comput. Appl. Math. 312, 216–230 (2017)

    Article  MathSciNet  Google Scholar 

  3. Mennichea, L., Benterki, D.: A logarithmic barrier approach for linear programming. J. Comput. Appl. Math. 312, 267–275 (2017)

    Article  MathSciNet  Google Scholar 

  4. Crouzeix, J.P., Merikhi, B.: A logarithm barrier method for semidefinite programming. RAIRO Oper. Res. 42, 123–139 (2008)

    Article  MathSciNet  Google Scholar 

  5. Alzalg, Baha, Pirhaji, M.: Elliptic cone optimization and primal-dual path-following algorithms. Optimization 66, 2245–2274 (2017)

    Article  MathSciNet  Google Scholar 

  6. Alzalg, Baha, Pirhaji, M.: Primal-dual path-following algorithms for circular programming. Commun. Comb. Optim. 2, 65–85 (2017)

    MathSciNet  MATH  Google Scholar 

  7. Wright, S.: Primal-Dual Interior Point Methods. SIAM, Philadelphia (1997)

    Book  Google Scholar 

  8. Hertog, D.: Interior-Point Approach to Linear Quadratic and Convex Programming. Kluwer, Dordrecht (1994)

    Book  Google Scholar 

  9. Nestrov, Y.E., Nemiroveskii, A.: Interior Point Polynomial Algorithms in Convex Programming. SIAM, Philadelphia (1994)

    Book  Google Scholar 

  10. Roos, C., Terlaky, T., Vial, J.P.: Theory and Algorithms for Linear Optimization: An Interior-Point Approach. Wiley, Hoboken (1997)

    MATH  Google Scholar 

  11. Ye, Y.: Interior Point Algorithms: Theory and Analysis. In: Wiley-Interscience Series in Discrete Mathematics Optimization. Wiley, New York (1997)

  12. Kebbiche, Z., Keraghel, A., Yassine, A.: Extension of a projective interior point method for linearly constrained convex programming. Appl. Math. Comput. 193, 553–559 (2007)

    MathSciNet  MATH  Google Scholar 

  13. Naseri, R., Valinejad, A.: An extended variant of Karmarkar’s interior point algorithm. Appl. Math. Comput. 184, 737–742 (2007)

    MathSciNet  MATH  Google Scholar 

  14. Achache, M.: A weighted path-following method for the linear complementarity problem. Studia Univ. Babes-Bolyai Ser. Inform. 48, 61–73 (2004)

    MathSciNet  MATH  Google Scholar 

  15. Darvay, Z.: A weighted-path-following method for linear optimization. Studia Univ. Babes-Bolyai Ser. Inform. 47, 3–12 (2002)

    MathSciNet  MATH  Google Scholar 

  16. Alizadeh, F., Haberly, J.-P., Overton, M.-L.: Primal-dual interior-point methods for semidefinite programming, convergence rates, stability and numerical results. SIAM J. Optim. 8, 746–768 (1998)

    Article  MathSciNet  Google Scholar 

  17. Benterki, D., Crouzeix, J.-P., Merikhi, B.: A numerical implementation of an interior point method for semi-definite programming. Pesquisa Operacional 23, 49–59 (2003)

    Article  Google Scholar 

  18. Tang, J., He, G., Dong, L., Fang, L., Zhou, J.: A globally and quadratically convergent smoothing Newton method for solving second-order cone optimization. Appl. Math. Model. 39, 2180–2193 (2015)

    Article  MathSciNet  Google Scholar 

  19. Dong, L., Tang, J., Zhou, J.: A smoothing Newton algorithm for solving the monotone second-order cone complementarity problems. J. Appl. Math. Comput. 40, 45–61 (2012)

    Article  MathSciNet  Google Scholar 

  20. Chi, X., Liu, S.: A new one-step smoothing Newton method for second-order cone programming. J. Comput. Appl. Math. 223, 114–123 (2009)

    Article  MathSciNet  Google Scholar 

  21. Tang, J., He, G., Dong, L., Fang, L.: A smoothing Newton method for second-order cone optimization based on a new smoothing function. Appl. Math. Comput. 218, 1317–1329 (2011)

    MathSciNet  MATH  Google Scholar 

  22. Fang, L.: A smoothing-type Newton method for second-order cone programming problems based on a new smooth function. J. Appl. Math. Comput. 34, 147–161 (2010)

    Article  MathSciNet  Google Scholar 

  23. Alizadeh, F., Goldfarb, D.: Second-order cone programming. Math. Program. Ser. B 95, 3–51 (2003)

    Article  MathSciNet  Google Scholar 

  24. Hans, Y.-J., Mittelmann, D.: Interior point methods for second-order cone programming and OR applications. Comput. Optim. Appl. 28, 255–285 (2004)

    Article  MathSciNet  Google Scholar 

  25. Kheirfam, B.: A corrector-predictor path-following method for second-order cone optimization. Int. J. Computer Math. 93, 2064–2078 (2016)

    Article  MathSciNet  Google Scholar 

  26. Hao, Z., Wan, Z., Chi, X., Chen, J.: A power penalty method for second-order cone nonlinear complementarity problems. J. Comput. Appl. Math. 290, 136–149 (2015)

    Article  MathSciNet  Google Scholar 

  27. Auslender, A.: An exact penalty method for nonconvex problems covering, in particular, nonlinear programming, semidefinite programming, and second-order cone programming. SIAM J. Optim. 25, 1732–1759 (2015)

    Article  MathSciNet  Google Scholar 

  28. Faraut, J., Korányi, A.: Analysis on Symmetric Cones. Oxford University Press, Oxford (1994)

    MATH  Google Scholar 

  29. Wolkowicz, H., Styan, G.-P.-H.: Bounds for eigenvalues using traces. Linear Algebra Appl. 29, 471–506 (1980)

    Article  MathSciNet  Google Scholar 

  30. Tang, J., He, G., Dong, L., Fang, L.: A new one-step smoothing newton method for second-order cone programming. Appl. Math. 57, 311–331 (2012)

    Article  MathSciNet  Google Scholar 

  31. Schmieta, S.H., Alizadeh, F.: Extension of primal-dual interior point methods to symmetric cones. Math. Program. Ser. A 96, 409–438 (2003)

    Article  Google Scholar 

  32. MOSEK is an optimization software designed to solve large-scale mathematical optimization problems. http://www.mosek.com/

Download references

Acknowledgements

The work of the author was supported in part by the Deanship of Scientific Research at The University of Jordan. The author thanks the anonymous referees for their valuable suggestions, their constructive comments have greatly enhanced the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Baha Alzalg.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Data statement

Due to the sensitive nature of the data, we are not in a position to share data as we do not have permission to do so.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alzalg, B. A logarithmic barrier interior-point method based on majorant functions for second-order cone programming. Optim Lett 14, 729–746 (2020). https://doi.org/10.1007/s11590-019-01404-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11590-019-01404-1

Keywords

Navigation