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Nondifferentiable Minimax Programming Problems in Complex Spaces Involving Generalized Convex Functions

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  • Anurag Jayswal
  • Ashish Kumar Prasad
  • Krishna Kummari

Abstract

We start our discussion with a class of nondifferentiable minimax programming problems in complex space and establish sufficient optimality conditions under generalized convexity assumptions. Furthermore, we derive weak, strong, and strict converse duality theorems for the two types of dual models in order to prove that the primal and dual problems will have no duality gap under the framework of generalized convexity for complex functions.

Suggested Citation

  • Anurag Jayswal & Ashish Kumar Prasad & Krishna Kummari, 2013. "Nondifferentiable Minimax Programming Problems in Complex Spaces Involving Generalized Convex Functions," Journal of Optimization, Hindawi, vol. 2013, pages 1-12, December.
    • Handle: RePEc:hin:jjopti:297015
    DOI: 10.1155/2013/297015
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    References listed on IDEAS

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    1. I. Ahmad & Z. Husain, 2006. "Optimality Conditions and Duality in Nondifferentiable Minimax Fractional Programming with Generalized Convexity," Journal of Optimization Theory and Applications, Springer, vol. 129(2), pages 255-275, May.
    2. I. Ahmad & Z. Husain & S. Sharma, 2009. "Higher-Order Duality in Nondifferentiable Minimax Programming with Generalized Type I Functions," Journal of Optimization Theory and Applications, Springer, vol. 141(1), pages 1-12, April.
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