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DOI: 10.23952/jnfa.2021.5
Received October 16, 2020; Accepted January 2, 2021; Published February 9, 2021
Abstract. In this paper, we consider a class of robust nondifferentiable minimax fractional programming problems containing support functions in both the objective functions and in the constraints. Using the robust subdifferentiable constraint qualification, we obtain necessary and sufficient optimality conditions for the robust convex-concave nondifferentiable minimax fractional problem. We introduce two types of robust dual problems, robust Wolfe dual and robust Mond-Weir dual. Moreover, we discuss the scenario uncertainty of a quadratic minimax fractional programming.
How to Cite this Article:
Indira P. Debnath, X. Qin, Robust optimality and duality for minimax fractional programming problems with support functions, Journal of Nonlinear Functional Analysis, Vol. 2021 (2021), Article ID 5, pp. 1-22.