Abstract
This paper is devoted to theoretical aspects in nonlinear optimization, in particular, duality relations for some mathematical programming problems. In this paper, we introduce a new generalized class of second-order multiobjective symmetric G-Wolfe-type model over arbitrary cones and establish duality results under G_{f}-bonvexity/G_{f}-pseudobonvexity assumptions. We construct nontrivial numerical examples which are G_{f}-bonvex/G_{f}-pseudobonvex but neither η-bonvex/η-pseudobonvex nor η-invex/η-pseudoinvex.
Highlights
It is an undeniable fact that all of us are optimizers as we all make decisions for the sole purpose of maximizing our quality of life, productivity in time, and our welfare in some way or another
Multiobjective optimization problems can be found in various fields such as product and process design, finance, aircraft design, the oil and gas industry, automobile design, and other where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives
Strong, and converse duality theorems by using Gf -bonvexity/Gf -pseudobonvexity assumptions over arbitrary cones
Summary
It is an undeniable fact that all of us are optimizers as we all make decisions for the sole purpose of maximizing our quality of life, productivity in time, and our welfare in some way or another. The concept of G-invex function is given by Antczak [3] and derived some duality results for a constrained optimization problem. Later on, generalizing his earlier work, Antzcak [4] introduced Gf invex functions for multivariate models and obtained optimality results for multiobjective programming problems. Bhatia and Garg [6] discussed the concept of (V , p) invexity for nonsmooth vector functions and established duality results for multiobjective programs.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
