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Optimality Conditions for Tanaka’s Approximate Solutions in Vector Optimization

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Generalized Convexity and Related Topics

Summary

In this work, approximate solutions of vector optimization problems in the sense of Tanaka [18] are characterized via scalarization. Necessary and sufficient conditions are obtained using a new order representing property and a new monotonicity concept, respectively. A family of gauge functions defined by generalized Chebyshev norms and verifying both properties is introduced in order to characterize approximate solutions of vector optimization problems via approximate solutions of several scalarizations.

This research was partially supported by the Ministerio de Ciencia y Tecnología (Spain), project BFM2003-02194.

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References

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

    Article  MATH  MathSciNet  Google Scholar 

  2. Dutta J, Vetrivel V (2001) On approximate minima in vector optimization. Numer. Funct. Anal. Optim. 22(7&8):845–859

    Article  MATH  MathSciNet  Google Scholar 

  3. Frenk JBG, Kassay G (1999) On classes of generalized convex functions, Gordan-Farkas type theorems, and Lagrangian duality. J. Optim. Theory Appl. 102(2):315–343

    Article  MATH  MathSciNet  Google Scholar 

  4. Gerth C, Weidner P (1990) Nonconvex separation theorems and some applications in vector optimization. J. Optim. Theory Appl. 67(2):297–320

    Article  MATH  MathSciNet  Google Scholar 

  5. Göpfert A, Riahi H, Tammer C, Zălinescu C (2003) Variational methods in partially ordered spaces. CMS Books in Mathematics. Springer, New York

    Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  7. Gutiérrez C, Jiménez B, Novo V (2005) A chain rule for ε-subdifferentials with applications to approximate solutions in convex Pareto problems. J. Math. Anal. Appl. 310:309–327

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  10. Jahn J (2004) Vector optimization. Theory, applications, and extensions. Springer, Berlin

    Google Scholar 

  11. Kutateladze SS (1979) Convex ε-programming. Soviet Math. Dokl. 20(2):391–393

    MATH  Google Scholar 

  12. Li Z, Wang S (1998) ε-approximate solutions in multiobjective optimization. Optimization 44:161–174

    MATH  MathSciNet  Google Scholar 

  13. Liu JC, Yokoyama K (1999) ε-optimality and duality for multiobjective fractional programming. Comput. Math. Appl. 37:119–128

    Article  CAS  MATH  MathSciNet  Google Scholar 

  14. Luc DT (1989) Theory of vector optimization. Lecture Notes in Econom. and Math. Systems 319, Springer-Verlag, Berlin

    MATH  Google Scholar 

  15. Rong WD (1997) Proper ε-efficiency in vector optimization problems with conesubconvexlikeness. Acta Sci. Natur. Univ. NeiMongol 28:609–613

    MathSciNet  Google Scholar 

  16. Rubinov A (1977) Sublinear operators and their applications. Russian Math. Surveys 32:115–175

    Article  MATH  MathSciNet  Google Scholar 

  17. Tammer C (1994) Stability results for approximately efficient solutions. OR Spektrum 16:47–52

    Article  MATH  MathSciNet  Google Scholar 

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

    Google Scholar 

  19. Vályi I (1985) Approximate solutions of vector optimization problems. In: Sydow A, Thoma M, Vichnevetsky R (eds) Systems analysis and simulation, pages 246–250. Akademie-Verlag, Berlin

    Google Scholar 

  20. White DJ (1986) Epsilon efficiency. J. Optim. Theory Appl. 49(2):319–337

    Article  MATH  MathSciNet  Google Scholar 

  21. Wierzbicki AP (1986) On the completeness and constructiveness of parametric characterizations to vector optimization problems. OR Spektrum 8:73–87

    Article  MATH  MathSciNet  Google Scholar 

  22. Yokoyama K (1996) Epsilon approximate solutions for multiobjective programming problems. J. Math. Anal. Appl. 203:142–149

    Article  MATH  MathSciNet  Google Scholar 

  23. Yokoyama K (1999) Relationships between efficient set and ε-efficient set. In: Proceedings of the International Conference on Nonlinear Analysis and Convex Analysis, 1999, pages 376–380. World Scientific Publishing, New York

    Google Scholar 

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Gutiérrez, C., Jiménez, B., Novo, V. (2007). Optimality Conditions for Tanaka’s Approximate Solutions in Vector Optimization. In: Generalized Convexity and Related Topics. Lecture Notes in Economics and Mathematical Systems, vol 583. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37007-9_16

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