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A generic approach to approximate efficiency and applications to vector optimization with set-valued maps

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Abstract

In this paper we focus on approximate minimal points of a set in Hausdorff locally convex spaces. Our aim is to develop a general framework from which it is possible to deduce important properties of these points by applying simple results. For this purpose we introduce a new concept of ε-efficient point based on set-valued mappings and we obtain existence results and properties on the behavior of these approximate efficient points when ε is fixed and by considering that ε tends to zero. Finally, the obtained results are applied to vector optimization problems with set-valued mappings.

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References

  1. Bolintinéanu S., Bonnel H.: Vector variational principles; ε-efficiency and scalar stationarity. J. Convex Anal 8, 71–85 (2001)

    Google Scholar 

  2. Chen G.Y., Huang X.X.: Ekeland’s ε-variational principle for set-valued mappings. Math. Methods Oper. Res. 48, 181–186 (1998)

    Article  Google Scholar 

  3. Chen G.Y., Huang X.X., Hou S.H.: General Ekeland’s variational principle for set-valued mappings. J. Optim. Theory Appl. 106, 151–164 (2000)

    Article  Google Scholar 

  4. Chinchuluun A., Pardalos P.M.: A survey of recent developments in multiobjective optimization. Ann. Oper. Res. 154, 29–50 (2007)

    Article  Google Scholar 

  5. Crespi G.P., Guerraggio A., Rocca M.: Well posedness in vector optimization problems and vector variational inequalities. J. Optim. Theory Appl. 132, 213–226 (2007)

    Article  Google Scholar 

  6. Deng S.: On approximate solutions in convex vector optimization. SIAM J. Control Optim. 35, 2128–2136 (1997)

    Article  Google Scholar 

  7. Dentcheva D., Helbig S.: On variational principles, level sets, well-posedness, and ε-solutions in vector optimization. J. Optim. Theory Appl. 89, 325–349 (1996)

    Article  Google Scholar 

  8. Durea M.: On the existence and stability of approximate solutions of perturbed vector equilibrium problems. J. Math. Anal. Appl. 333, 1165–1179 (2007)

    Article  Google Scholar 

  9. Dutta J., Vetrivel V.: On approximate minima in vector optimization. Numer. Funct. Anal. Optim. 22, 845–859 (2001)

    Article  Google Scholar 

  10. Göpfert A., Riahi H., Tammer C., Zălinescu C.: Variational Methods in Partially Ordered Spaces. Springer, New York (2003)

    Google Scholar 

  11. Govil M.G., Mehra A.: ε-optimality for multiobjective programming on a Banach space. European J. Oper. Res. 157, 106–112 (2004)

    Article  Google Scholar 

  12. Gutiérrez C., Jiménez B., Novo V.: Multiplier rules and saddle-point theorems for Helbig’s approximate solutions in convex Pareto problems. J. Global Optim. 32, 367–383 (2005)

    Article  Google Scholar 

  13. Gutiérrez C., Jiménez B., Novo V.: On approximate efficiency in multiobjective programming. Math. Methods Oper. Res. 64, 165–185 (2006)

    Article  Google Scholar 

  14. Gutiérrez C., Jiménez B., Novo V.: On approximate solutions in vector optimization problems via scalarization. Comput. Optim. Appl. 35, 305–324 (2006)

    Article  Google Scholar 

  15. Gutiérrez C., Jiménez B., Novo V.: A unified approach and optimality conditions for approximate solutions of vector optimization problems. SIAM J. Optim. 17, 688–710 (2006)

    Article  Google Scholar 

  16. Gutiérrez C., Jiménez B., Novo V.: Optimality conditions for metrically consistent approximate solutions in vector optimization. J. Optim. Theory Appl. 133, 49–64 (2007)

    Article  Google Scholar 

  17. Gutiérrez C., Jiménez B., Novo V.: A set-valued Ekeland’s variational principle in vector optimization. SIAM J. Control Optim. 47, 883–903 (2008)

    Article  Google Scholar 

  18. Gutiérrez C., Jiménez B., Novo V.: Optimality conditions via scalarization for a new ε-efficiency concept in vector optimization problems. European J. Oper. Res. 201, 11–22 (2010)

    Article  Google Scholar 

  19. Helbig S., Pateva D.: On several concepts for ε-efficiency. OR Spektrum 16, 179–186 (1994)

    Article  Google Scholar 

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

    Google Scholar 

  21. Kutateladze S.S.: Convex ε-programming. Soviet Math. Dokl. 20, 391–393 (1979)

    Google Scholar 

  22. Loridan P.: ε-Solutions in vector minimization problems. J. Optim. Theory Appl. 43, 265–276 (1984)

    Article  Google Scholar 

  23. Németh A.B.: A nonconvex vector minimization problem. Nonlinear Anal. 10, 669–678 (1986)

    Article  Google Scholar 

  24. Németh A.B.: Between Pareto efficiency and Pareto ε-efficiency. Optimization 20, 615–637 (1989)

    Article  Google Scholar 

  25. Norde H., Patrone F., Tijs S.: Characterizing properties of approximate solutions for optimization problems. Math. Soc. Sci. 40, 297–311 (2000)

    Article  Google Scholar 

  26. Norde H., Patrone F., Tijs S.: Axiomatization for approximate solutions in optimization, in quilibrium problems: nonsmooth optimization and variational inequality models. Nonconvex Optim. Appl. 58, 207–221 (2002)

    Article  Google Scholar 

  27. Rong W.D., Wu Y.N.: ε-weak minimal solutions of vector optimization problems with set-valued maps. J. Optim. Theory Appl. 106, 569–579 (2000)

    Article  Google Scholar 

  28. Sawaragi Y., Nakayama H., Tanino T.: Theory of Multiobjective Optimization. Academic Press, Orlando (1985)

    Google Scholar 

  29. Staib T.: On two generalizations of Pareto minimality. J. Optim. Theory Appl. 59, 289–306 (1988)

    Google Scholar 

  30. Tanaka T.: A new approach to approximation of solutions in vector optimization problems. In: Fushimi, M., Tone, K. (eds) Proceedings of APORS 1994, pp. 497–504. World Scientific Publishing, Singapore (1995)

    Google Scholar 

  31. Vályi I.: Approximate solutions of vector optimization problems. In: Sydow, A., Thoma, M., Vichnevetsky, R. (eds) Systems Analysis and Simulation, pp. 246–250. Akademie, Berlin (1985)

    Google Scholar 

  32. White D.J.: Epsilon efficiency. J. Optim. Theory Appl. 49, 319–337 (1986)

    Article  Google Scholar 

  33. Yokoyama K.: Relationships between efficient set and ε-efficient set. In: Takahashi, W., Tanaka, T. (eds) Nonlinear Analysis and Convex Analysis, pp. 376–380. World Scientific Publishing, River Edge (1999)

    Google Scholar 

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Gutiérrez, C., Jiménez, B. & Novo, V. A generic approach to approximate efficiency and applications to vector optimization with set-valued maps. J Glob Optim 49, 313–342 (2011). https://doi.org/10.1007/s10898-010-9546-4

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  • DOI: https://doi.org/10.1007/s10898-010-9546-4

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