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The Study Higher-order Wolfe-type Non-differentiable Multiple Objective Symmetric Duality Involving Generalized Convex Functions

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  • Arun Kumar Tripathy

    (Sri Sri Bayababa College)

Abstract

In this paper, a new class of generalized $$K-({\Phi}{,{\rho}})$$ K - ( Φ , ρ ) convex function is introduced, in which the sublinearity property of F as in literature is relaxed by imposing the convexity assumption on $$\phi$$ ϕ in its third argument with an example. This new class of generalized convex function is more generalized than the $$(F,\alpha ,\rho ,d)$$ ( F , α , ρ , d ) -convex functions, $$(C,\alpha ,\rho ,d)$$ ( C , α , ρ , d ) -convex functions and $$K-(F,\alpha ,\rho ,d)$$ K - ( F , α , ρ , d ) convex functions studied in literature. Also, a new model of higher-order Wolfe-type non-differentiable multi-objective symmetric dual programs is presented and the weak, strong, and converse duality theorem under higher-order $$K-({\Phi}{,{\rho}})$$ K - ( Φ , ρ ) convex functions are established. Some special cases which generalizes our results is discussed.

Suggested Citation

  • Arun Kumar Tripathy, 2021. "The Study Higher-order Wolfe-type Non-differentiable Multiple Objective Symmetric Duality Involving Generalized Convex Functions," SN Operations Research Forum, Springer, vol. 2(4), pages 1-18, December.
  • Handle: RePEc:spr:snopef:v:2:y:2021:i:4:d:10.1007_s43069-021-00090-z
    DOI: 10.1007/s43069-021-00090-z
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    References listed on IDEAS

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    1. Igor V. Konnov & Dinh The Luc & Alexander M. Rubinov, 2006. "Generalized Convexity and Related Topics," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-37007-9, July.
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    3. Suneja, S. K. & Aggarwal, Sunila & Davar, Sonia, 2002. "Multiobjective symmetric duality involving cones," European Journal of Operational Research, Elsevier, vol. 141(3), pages 471-479, September.
    4. Mishra, S. K., 2005. "Non-differentiable higher-order symmetric duality in mathematical programming with generalized invexity," European Journal of Operational Research, Elsevier, vol. 167(1), pages 28-34, November.
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