Mixed symmetric duality in non-differentiable multiobjective mathematical programming
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 1-9 |
Journal / Publication | European Journal of Operational Research |
Volume | 181 |
Issue number | 1 |
Publication status | Published - 16 Aug 2007 |
Link(s)
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. et al.
In: European Journal of Operational Research, Vol. 181, No. 1, 16.08.2007, p. 1-9.
In: European Journal of Operational Research, Vol. 181, No. 1, 16.08.2007, p. 1-9.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review