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Variable Ordering Structures in Vector Optimization

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Recent Developments in Vector Optimization

Part of the book series: Vector Optimization ((VECTOROPT,volume 1))

Abstract

In vector optimization one assumes in general that a partial ordering is given by some nontrivial convex cone K in the considered space Y. But already in 1974 in one of the first publications [37] related to the definition of optimal elements in vector optimization also the idea of variable ordering structures was given: to each element of the space a cone of dominated (or preferred) directions is defined and thus the ordering structure is given by a set-valued map. In [37] a candidate element was defined to be nondominated if it is not dominated by any other reference element w.r.t. the corresponding cone of this other element. Later, also another notion of optimal elements in the case of a variable ordering structure was introduced [7,  8,  9]: a candidate element is called a minimal (or nondominated-like) element if it is not dominated by any other reference element w.r.t. the cone of the candidate element.

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References

  1. Al-Homodan, S., Ansari, Q.H., Schaible, S.: Existence of solutions of systems of generalized implicit vector variational inequalities. J. Optim. Theory Appl. 134, 515–531 (2007)

    Article  Google Scholar 

  2. Ansari, Q.H., Yao, J.C.: On nondifferentiable and nonconvex vector optimization problems. J. Optim. Theory Appl. 106, 475–488 (2000)

    Article  Google Scholar 

  3. Baatar, D., Wiecek, M.M.: Advancing equitability in multiobjective programming. Comput. Math. Appl. 52, 225–234 (2006)

    Article  Google Scholar 

  4. Bergstresser, K., Charnes, A., Yu, P.L.: Generalization of domination structures and nondominated solutions in multicriteria decision making. J. Optim. Theory Appl. 18, 3–13 (1976)

    Article  Google Scholar 

  5. Bishop, E., Phelps, R.R.: The support functionals of a convex set. Proc. Sympos. Pure Math. 7, 27–35 (1962)

    Google Scholar 

  6. Ceng, L.-C., Huang, S.: Existence theorems for generalized vector variational inequalities with a variable ordering relation. J. Global Optim. 46, 521–535 (2010)

    Article  Google Scholar 

  7. Chen, G.Y.: Existence of solutions for a vector variational inequality: an extension of the Hartmann-Stampacchia Theorem. J. Optim. Theory Appl. 74, 445–456 (1992)

    Article  Google Scholar 

  8. Chen, G.Y., Huang, X., Yang, X.: Vector Optimization, Set-Valued and Variational Analysis. Springer, Berlin (2005)

    Google Scholar 

  9. Chen, G.Y., Yang, X.Q.: Characterizations of variable domination structures via nonlinear scalarization. J. Optim. Theory Appl. 112, 97–110 (2002)

    Article  Google Scholar 

  10. Chen, G.Y., Yang, X.Q., Yu, H.: A nonlinear scalarization function and generalized quasi-vector equilibrium problems. J. Global Optim. 32, 451–466 (2005)

    Article  Google Scholar 

  11. Chew, K.L.: Domination structures in abstract spaces. Southeast Asian Bulletin of Mathematics. Proc. first franco-southeast Asian Math. Conference, 190–204 (1979)

    Google Scholar 

  12. Eichfelder, G.: Adaptive Scalarization Methods in Multiobjective Optimization. Springer, Berlin (2008)

    Book  Google Scholar 

  13. Eichfelder, G.: An adaptive scalarization method in multi-objective optimization. SIAM J. Optim. 19, 1694–1718 (2009)

    Article  Google Scholar 

  14. Eichfelder, G.: Optimal elements in vector optimization with a variable ordering structure. J. Optim. Theory and Appl. 151 (2011)

    Google Scholar 

  15. Eichfelder, G., Ha, T.X.D.: Optimality conditions for vector optimization problems with variable ordering structures. Optimization (2011) doi: 10.1080/02331934.2011.579939 Preprint series of the Institute of Applied Mathematics, Univ. Erlangen-Nürnberg 338 (2010)

    Google Scholar 

  16. Engau, A.: Variable preference modeling with ideal-symmetric convex cones. J. Global Optim. 42, 295–311 (2008)

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  19. Ha, T.X.D.: Optimality conditions for several types of efficient solutions of set-valued optimization problems. In: Nonlinear Analysis and Variational Problems, Eds. P. Pardalos, Th.M. Rassias, A.A. Khan. Springer, 305–324 (2009)

    Google Scholar 

  20. Hiriart-Urruty, J-B.: New concepts in nondifferentiable programming. Bull. Soc. Math. France 60, 57–85 (1979)

    Google Scholar 

  21. Huang, N.J., Yang, X.Q., Chan, W.K.: Vector complementarity problems with a variable ordering relation. Eur. J. Oper. Res. 176, 15–26 (2007)

    Article  Google Scholar 

  22. Jahn, J.: Vector Optimization - Theory, Applications, and Extensions. Springer, Heidelberg (2004)

    Google Scholar 

  23. Jahn, J.: Bishop-Phelps cones in optimization. International J. Optim.: Theory, Meth. Appl. 1, 123–139 (2009)

    Google Scholar 

  24. Kasimbeyli, R.: A nonlinear cone separation theorem and scalarization in nonconvex vector optimization. SIAM J. Optim. 20, 1591–1619 (2010)

    Article  Google Scholar 

  25. Kostreva, M.M., Ogryczak, W., Wierzbicki, A.: Equitable aggregations in multiple criteria analysis. Eur. J. Oper. Res. 158, 362–377 (2004)

    Article  Google Scholar 

  26. Krasnoselskii, M.A.: Positive solutions of operator equations. Noordhoff, Groningen (1964)

    Google Scholar 

  27. Lee, G.M., Kim, D.S., Lee, B.S.: On noncooperative vector equilibrium. Indian J. Pure Appl. Math. 27, 735–739 (1996)

    Google Scholar 

  28. Lee, G.M., Kim, D.S., Kuk, H.: Existence of solutions for vector optimization problems. J. Math. Anal. Appl. 220, 90–98 (1998)

    Article  Google Scholar 

  29. Liu, C.G., Ng, K.F., Yang, W.H.: Merit functions in vector optimization. Math. Prog. Ser. A 119, 215–237 (2009)

    Article  Google Scholar 

  30. Ogryczak, W., Sliwinski, T.: On solving linear programs with the ordered weighted averaging objective. Eur. J. Oper. Res. 148, 80–91 (2003)

    Article  Google Scholar 

  31. Pascoletti, A., Serafini, P.: Scalarizing vector optimization problems. J. Optim. Theory Appl. 42, 499–524 (1984)

    Article  Google Scholar 

  32. Petschke, M.: On a theorem of Arrow, Barankin, and Blackwell. SIAM J. Control Optim. 28, 395–401 (1990)

    Article  Google Scholar 

  33. Thieke, C.: Private communication (2010)

    Google Scholar 

  34. Wacker, M.: Multikriterielle Optimierung bei Registrierung medizinischer Daten. Diplomarbeit, Univ. Erlangen-Nürnberg, Germany (2008)

    Google Scholar 

  35. Wacker, M., Deinzer, F.: Automatic robust medical image registration using a new democratic vector optimization approach with multiple measures. In: Medical Image Computing and Computer-Assisted Intervention MICCAI 2009, Eds. G.-Z. Yang et al., 590–597 (2009)

    Google Scholar 

  36. Xiao, G., Xiao, H., Liu, S.: Scalarization and pointwise well-posedness in vector optimization problems. J. Global Optim. 49, 561–574 (2011)

    Article  Google Scholar 

  37. Yu, P.L.: Cone convexity, cone extreme points, and nondominated solutions in decision problems with multiobjectives. J. Optim. Theory Appl. 14, 319–377 (1974)

    Article  Google Scholar 

  38. Yu, P.L.: Multiple-criteria Decision Making: Concepts, Techniques and Extensions. Plenum Press, New York (1985)

    Google Scholar 

  39. Zaffaroni, A.: Degreesof efficiency and degreees of minimality. SIAM J. Control Optim. 42, 1071–1086 (2003)

    Article  Google Scholar 

  40. Zheng, F.: Vector variational inequalities with semi-monotone Operators. J. Global Optim. 32, 633–642 (2005)

    Article  Google Scholar 

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Acknowledgements

This contribution was partially written during the author’s stay at the Institute of Mathematics, Hanoi, Vietnam, which was supported by the Alexander von Humboldt-Foundation and the Institute of Mathematics of Hanoi. The author thanks Prof. Dr. Truong Xuan Duc Ha for hospitality and discussions on the topic of this chapter.

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Correspondence to Gabriele Eichfelder .

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Eichfelder, G. (2012). Variable Ordering Structures in Vector Optimization. In: Ansari, Q., Yao, JC. (eds) Recent Developments in Vector Optimization. Vector Optimization, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21114-0_4

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