Skip to main content
Log in

Selective bi-coordinate variations for resource allocation type problems

  • Published:
Computational Optimization and Applications Aims and scope Submit manuscript

Abstract

We suggest a modification of the coordinate descent methods for resource allocation problems, which keeps the basic convergence properties of the gradient ones, but enables one to reduce the total computational expenses and to provide all the computations in a distributed manner. We describe applications to economic (auction) equilibrium problems and give preliminary results of computational tests.

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

Notes

  1. The papers [15, 19] were found by the author after preparing the first version of this manuscript, whereas the papers [1618, 20] were suggested by an anonymous referee.

References

  1. Bertsekas, D.P., Tsitsiklis, J.N.: Parallel and Distributed Computation: Numerical Methods. Prentice-Hall, London (1989)

    MATH  Google Scholar 

  2. Patriksson, M.: Nonlinear Programming and Variational Inequality Problems: A Unified Approach. Kluwer Academic Publishers, Dordrecht (1999)

    Book  MATH  Google Scholar 

  3. Konnov, I.V.: Equilibrium Models and Variational Inequalities. Elsevier, Amsterdam (2007)

    MATH  Google Scholar 

  4. Courcoubetis, C., Weber, R.: Pricing Communication Networks: Economics, Technology and Modelling. Wiley, Chichester (2003)

    Book  Google Scholar 

  5. Stańczak, S., Wiczanowski, M., Boche, H.: Resource Allocation in Wireless Networks. Theory and Algorithms. Springer, Berlin (2006)

    Book  MATH  Google Scholar 

  6. Lobel, I., Ozdaglar, A., Feijer, D.: Distributed multi-agent optimization with state-dependent communication. Math. Program. 129, 255–284 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  7. Peng, Z., Yan, M., Yin, W.: Parallel and distributed sparse optimization. In: The 47th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, pp. 646–659. IEEE (2013)

  8. Beraldi, P., Conforti, D., Triki, C., Violi, A.: Constrained auction clearing in the Italian electricity market. 4OR 2, 35–51 (2004)

    MathSciNet  MATH  Google Scholar 

  9. Huang, J., Berry, R.A., Honig, M.L.: Auction-based spectrum sharing. Mob. Netw. Appl. 11, 405–418 (2006)

    Article  Google Scholar 

  10. Wang, X., Li, Z., Xu, P., Xu, Y., Gao, X., Chen, H.-H.: Spectrum sharing in cognitive radio networks: an auction based approach. IEEE Syst. Man Cybern. Part B 40, 587–596 (2010)

    Article  Google Scholar 

  11. Konnov, I.V.: Equilibrium models for multi-commodity auction market problems. Adv. Model. Optim. 15, 511–524 (2013)

    Google Scholar 

  12. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Know. Disc. 2, 121–167 (1998)

    Article  Google Scholar 

  13. Richtárik, P., Takáč, M.: Parallel coordinate descent methods for big data optimization. arXiv:1212.0873v2. http://arxiv.org/pdf/1212.0873 (2013). Accessed 25 Nov 2013

  14. Cevher, V., Becker, S., Schmidt, M.: Convex optimization for big data. Signal Process. Magaz. 31, 32–43 (2014)

    Article  Google Scholar 

  15. Korpelevich, G.M.: Coordinate descent method for minimization problems with linear inequality constraints and matrix games. In: Gol’shtein, E.G. (ed.) Mathematical Methods for Solving Economic Problems, vol. 9, pp. 84–97. Nauka, Moscow (1980). [In Russian]

    Google Scholar 

  16. Lin, C.J., Lucidi, S., Palagi, L., Risi, A., Sciandrone, A.: Decomposition algorithm model for singly linearly constrained problems subject to lower and upper bounds. J. Optim. Theory Appl. 141, 107–126 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  17. Necoara, I.: Random coordinate descent algorithms for multi-agent convex optimization over networks. IEEE Trans. Autom. Contr. 58, 2001–2012 (2013)

    Article  MathSciNet  Google Scholar 

  18. Necoara, I., Patrascu, A.: A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints. Comput. Optim. Appl. 57, 307–337 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  19. Beck, A.: The 2-coordinate descent method for solving double-sided simplex constrained minimization problems. J. Optim. Theory. Appl. 162, 892–919 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  20. Patrascu, A., Necoara, I.: Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization. J. Glob. Optim. 61, 19–46 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  21. Konnov, I.V.: Combined relaxation method for decomposable variational inequalities. Optimiz. Meth. Softw. 10, 711–728 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  22. Konnov, I.V.: A class of combined relaxation methods for decomposable variational inequalities. Optimization 51, 109–125 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  23. Allevi, E., Gnudi, A., Konnov, I.V.: Combined relaxation method with Frank–Wolfe type auxiliary procedures for variational inequalities over product sets. Pure Math. Appl. 12, 1–9 (2001)

    MathSciNet  MATH  Google Scholar 

  24. Konnov, I.V.: Nonlinear Optimization and Variational Inequalities. Kazan University Press, Kazan (2013). [In Russian]

    Google Scholar 

  25. Dem’yanov, V.F., Rubinov, A.M.: Approximate Methods for Solving Extremum Problems. Leningrad University Press, Leningrad (1968). [In Russian]; Engl. transl. in Elsevier, Amsterdam (1970)

    Google Scholar 

  26. Arrow, K.J., Hahn, F.H.: General Competitive Analysis. Holden Day, New York (1971)

    MATH  Google Scholar 

  27. Frank, M., Wolfe, P.: An algorithm for quadratic programming. Nav. Res. Logist. Quart. 3, 95–110 (1956)

    Article  MathSciNet  Google Scholar 

  28. Levitin, E.S., Polyak, B.T.: Constrained minimization methods. USSR Comput. Maths. Math. Phys. 6, 1–50 (1966)

    Article  Google Scholar 

Download references

Acknowledgments

The author is grateful to the anonymous referees for their valuable comments. In particular, he is thankful to the one anonymous referee for drawing the author’s attention to the papers [1618, 20].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to I. V. Konnov.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Konnov, I.V. Selective bi-coordinate variations for resource allocation type problems. Comput Optim Appl 64, 821–842 (2016). https://doi.org/10.1007/s10589-016-9824-2

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10589-016-9824-2

Keywords

Mathematics Subject Classification

Navigation