Skip to main content
Log in

Robust second order cone conditions and duality for multiobjective problems under uncertainty data

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

This paper studies a class of multiobjective convex polynomial problems, where both the constraint and objective functions involve uncertain parameters that reside in ellipsoidal uncertainty sets. Employing the robust deterministic approach, we provide necessary conditions and sufficient conditions, which are exhibited in relation to second order cone conditions, for robust (weak) Pareto solutions of the uncertain multiobjective optimization problem. A dual multiobjective problem is proposed to examine robust converse, robust weak and robust strong duality relations between the primal and dual problems. Moreover, we establish robust solution relationships between the uncertain multiobjective optimization program and a (scalar) second order cone programming relaxation problem of a corresponding weighted-sum optimization problem. This in particular shows that we can find a robust (weak) Pareto solution of the uncertain multiobjective optimization problem by solving a second order cone programming relaxation.

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. Ahmadi, A.A., Majumdar, A.: Some applications of polynomial optimization in operations research and real-time decision making. Optim. Lett. 10(4), 709–729 (2016)

    Article  MathSciNet  Google Scholar 

  2. Ahmadi, A.A., Majumdar, A.: DSOS and SDSOS optimization: more tractable alternatives to sum of squares and semidefinite optimization. SIAM J. Appl. Algebra Geom. 3, 193–230 (2019)

    Article  MathSciNet  Google Scholar 

  3. Ahmadi, A.A., Parrilo, P.A.: A complete characterization of the gap between convexity and sos-convexity. SIAM J. Optim. 23(2), 811–833 (2013)

    Article  MathSciNet  Google Scholar 

  4. Blekherman, G., Parrilo, P.A., Thomas, R.: Semidefinite Optimization and Convex Algebraic Geometry. SIAM Publications, Philadelphia (2012)

    Book  Google Scholar 

  5. Ben-Tal, A., El Ghaoui, L., Nemirovski, A.: Robust Optimization. Princeton University Press, Princeton, NJ (2009)

    Book  Google Scholar 

  6. Ben-Tal, A., den Hertog, D., Vial, J.-P.: Deriving robust counterparts of nonlinear uncertain inequalities. Math. Program. 149(1–2), 265–299 (2015)

    Article  MathSciNet  Google Scholar 

  7. Bertsimas, D., Brown, D.B., Caramanis, C.: Theory and applications of robust optimization. SIAM Rev. 53, 464–501 (2011)

    Article  MathSciNet  Google Scholar 

  8. Chuong, T.D., Jeyakumar, V., Li, G.: A new bounded degree hierarchy with SOCP relaxations for global polynomial optimization and conic convex semi-algebraic programs. J. Global Optim. 75, 885–919 (2019)

    Article  MathSciNet  Google Scholar 

  9. Chuong, T.D.: Optimality and duality for robust multiobjective optimization problems. Nonlinear Anal. 134, 127–143 (2016)

    Article  MathSciNet  Google Scholar 

  10. Chuong, T.D.: Robust alternative theorem for linear inequalities with applications to robust multiobjective optimization. Oper. Res. Lett. 45(6), 575–580 (2017)

    Article  MathSciNet  Google Scholar 

  11. Chuong, T.D.: Linear matrix inequality conditions and duality for a class of robust multiobjective convex polynomial programs. SIAM J. Optim. 28, 2466–2488 (2018)

    Article  MathSciNet  Google Scholar 

  12. Chuong, T.D.: Robust optimality and duality in multiobjective optimization problems under data uncertainty. SIAM J. Optim. 30, 1501–1526 (2020)

    Article  MathSciNet  Google Scholar 

  13. Chuong, T.D.: Second-order cone programming relaxations for a class of multiobjective convex polynomial problems. Ann. Oper. Res. 311, 1017–1033 (2022)

    Article  MathSciNet  Google Scholar 

  14. Chuong, T.D., Jeyakumar, V.: Adjustable robust multi-objective linear optimization: pareto optimal solutions via conic programming. Ann. Oper. Res. (2022). https://doi.org/10.1007/s10479-022-05104-5

    Article  Google Scholar 

  15. Ehrgott, M.: Multicriteria Optimization. Springer, Berlin (2005)

    Google Scholar 

  16. Ehrgott, M., Ide, J., Schobel, A.: Minmax robustness for multi-objective optimization problems. Eur. J. Oper. Res. 239, 17–31 (2014)

    Article  MathSciNet  Google Scholar 

  17. Georgiev, P.G., Luc, D.T., Pardalos, P.M.: Robust aspects of solutions in deterministic multiple objective linear programming. Eur. J. Oper. Res. 229(1), 29–36 (2013)

    Article  MathSciNet  Google Scholar 

  18. Goberna, M.A., Jeyakumar, V., Li, G., Perez, J.-V.: Robust solutions of multi-objective linear semi-infinite programs under constraint data uncertainty. SIAM J. Optim. 24(3), 1402–1419 (2014)

    Article  MathSciNet  Google Scholar 

  19. Jahn, J.: Vector Optimization: Theory, Applications, and Extensions. Springer, Berlin (2004)

    Book  Google Scholar 

  20. Gorissen, B.L., den Hertog, D.: Approximating the Pareto sets of multiobjective linear programs via robust optimizaton. Oper. Res. Lett. 40(5), 319–324 (2012)

    Article  MathSciNet  Google Scholar 

  21. Helton, J.W., Nie, J.: Semidefinite representation of convex sets. Math. Program. 122(1), 21–64 (2010)

    Article  MathSciNet  Google Scholar 

  22. Kuroiwa, D., Lee, G.M.: On robust multiobjective optimization. Vietnam J. Math. 40(2–3), 305–317 (2012)

    MathSciNet  Google Scholar 

  23. La Torre, D., Mendivil, F.: Portfolio optimization under partial uncertainty and incomplete information: a probability multimeasure-based approach. Ann. Oper. Res. 267(1–2), 267–279 (2018)

    Article  MathSciNet  Google Scholar 

  24. Lee, G.M., Lee, J.H.: On nonsmooth optimality theorems for robust multiobjective optimization problems. J. Nonlinear Convex Anal. 16(10), 2039–2052 (2015)

    MathSciNet  Google Scholar 

  25. Lee, J.H., Jiao, L.: Finding efficient solutions in robust multiple objective optimization with SOS-convex polynomial data. Ann. Oper. Res. 296, 803–820 (2021)

    Article  MathSciNet  Google Scholar 

  26. Lee, J.H., Lee, G.M.: On optimality conditions and duality theorems for robust semi-infinite multiobjective optimization problems. Ann. Oper. Res. 269(1–2), 419–438 (2018)

    Article  MathSciNet  Google Scholar 

  27. Luc, D.T.: Theory of Vector Optimization Lecture Notes in Economics and Mathematical Systems, vol. 319. Springer, Berlin (1989)

    Google Scholar 

  28. Magron, V., Henrion, D., Lasserre, J.-B.: Approximating Pareto curves using semidefinite relaxations. Oper. Res. Lett. 42(6–7), 432–437 (2014)

    Article  MathSciNet  Google Scholar 

  29. Miettinen, K.: Nonlinear Multiobjective Optimization, Vol. 12. Kluwer Academic Publishers (1999)

  30. Mordukhovich, B. S., Nam, N. M.: An easy path to convex analysis and applications, Synthesis Lectures on Mathematics and Statistics. 14. Morgan & Claypool Publishers, Williston (2014)

  31. Pardalos, P.M., Zilinskas, A., Zilinskas, J.: Non-convex Multi-objective Optimization. Springer Optimization and its Applications, vol. 123. Springer, Cham (2017)

    Google Scholar 

  32. Rockafellar, R.T.: Convex Analysis. Princeton University Press, Princeton, NJ (1970)

    Book  Google Scholar 

  33. Sion, M.: On general minimax theorems. Pacific J. Math. 8, 171–176 (1958)

    Article  MathSciNet  Google Scholar 

  34. Soyster, A.: Convex programming with set-inclusive constraints and applications to inexact linear programming. Oper. Res. 1, 1154–1157 (1973)

    Article  Google Scholar 

  35. Steuer, R.E.: Multiple Criteria Optimization. Theory, Computation, and Application. Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics. Wiley, New York (1986)

  36. Zamani, M., Soleimani-damaneh, M., Kabgani, A.: Robustness in nonsmooth nonlinear multi-objective programming. Eur. J. Oper. Res. 247(2), 370–378 (2015)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors are grateful to a referee for the valuable comments and suggestions. This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number T2024-26-01.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cao Thanh Tinh.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tinh, C.T., Chuong, T.D. Robust second order cone conditions and duality for multiobjective problems under uncertainty data. J Glob Optim 88, 901–926 (2024). https://doi.org/10.1007/s10898-023-01335-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-023-01335-3

Keywords

Mathematics Subject Classification

Navigation