A new characterization of (weak) Pareto optimality for differentiable vector optimization problems with G-invex functions

Authors:
Tadeusz Antczak
Affiliations:
-
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2011

Citing 6
Cited 0

Optimality conditions in multiobjective differentiable programming

Journal of Optimization Theory and Applications
Optimality criteria and duality in multiple-objective optimization involving generalized invexity

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A characterization of weakly efficient points

Mathematical Programming: Series A and B
Invex functions and generalized convexity in multiobjective programming

Journal of Optimization Theory and Applications
On G-invex multiobjective programming. Part I. Optimality

Journal of Global Optimization
On G-invex multiobjective programming. Part II. Duality

Journal of Global Optimization

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Abstract

In this paper, the so-called @h-approximation approach is used to characterize solvability (in Pareto sense) of a nonlinear multiobjective programming problem with G-invex functions with respect to the same function @h. In this method, an equivalent @h-approximated vector optimization problem is constructed by a modification of both the objective and the constraint functions in the original multiobjective programming problem at the given feasible point. Moreover, in order to find a (weak) Pareto optimal solution in the original multiobjective problem, it is sufficient to solve its associated @h-approximated vector optimization problem equivalent to the original multiobjective programming problem in the sense discussed in this paper.