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Duality on a nondifferentiable minimax fractional programming

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  • Hang-Chin Lai

    ()

  • Hui-Mei Chen

    ()

Abstract

We establish the necessary and sufficient optimality conditions on a nondifferentiable minimax fractional programming problem. Subsequently, applying the optimality conditions, we constitute two dual models: Mond-Weir type and Wolfe type. On these duality types, we prove three duality theorems—weak duality theorem, strong duality theorem, and strict converse duality theorem. Copyright Springer Science+Business Media, LLC. 2012

Suggested Citation

  • Hang-Chin Lai & Hui-Mei Chen, 2012. "Duality on a nondifferentiable minimax fractional programming," Journal of Global Optimization, Springer, vol. 54(2), pages 295-306, October.
  • Handle: RePEc:spr:jglopt:v:54:y:2012:i:2:p:295-306 DOI: 10.1007/s10898-010-9631-8
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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