Mixed symmetric duality in non-differentiable multiobjective mathematical programming

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

2 Scopus Citations
View graph of relations

Author(s)

  • S. K. Mishra
  • S. Y. Wang
  • K. K. Lai
  • F. M. Yang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1-9
Journal / PublicationEuropean Journal of Operational Research
Volume181
Issue number1
Publication statusPublished - 16 Aug 2007

Abstract

Two mixed symmetric dual models for a class of non-differentiable multiobjective nonlinear programming problems with multiple arguments are introduced in this paper. These two mixed symmetric dual models unify the four existing multiobjective symmetric dual models in the literature. Weak and strong duality theorems are established for these models under some mild assumptions of generalized convexity. Several special cases are also obtained. © 2006 Elsevier B.V. All rights reserved.

Research Area(s)

  • Generalized convexity, Non-differentiable nonlinear programming, Support function, Symmetric duality

Citation Format(s)

Mixed symmetric duality in non-differentiable multiobjective mathematical programming. / Mishra, S. K.; Wang, S. Y.; Lai, K. K.; Yang, F. M.

In: European Journal of Operational Research, Vol. 181, No. 1, 16.08.2007, p. 1-9.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal