WebJan 31, 2024 · Via robust optimization, we establish the necessary and sufficient optimality conditions for an uncertain minimax convex-concave fractional programming problem under the robust subdifferentiable constraint qualification. ... A. Beck and A. Ben-Tal, Duality in robust optimization: Primal worst equals dual best, Oper. Res. Lett., 37 (2009), 1-6 ... WebJul 16, 2013 · Following the framework of robust optimization, Jeyakumar et al. [12] developed a duality theory for a minimax fractional optimization problem in the face of data uncertainty both in the objective ...
Strong duality for robust minimax fractional programming …
WebApr 1, 2024 · In this paper, we reformulate the original adjustable robust nonlinear problem with a polyhedral uncertainty set into an equivalent adjustable robust linear problem, for which all existing approaches for adjustable robust linear problems can be used. The reformulation is obtained by first dualizing over the adjustable variables and then over ... WebJan 31, 2009 · To do so, extending results from robust optimization duality [4], an optimistic dual counterpart problem is derived and robust strong duality is shown to … rainbow shoreditch
Strong duality in robust semi-definite linear programming under …
WebApr 30, 2024 · We present a short and elementary proof of the duality for Wasserstein distributionally robust optimization, which holds for any arbitrary Kantorovich transport distance, measurable loss function and nominal probability distribution, so long as certain interchangeability condition holds. As an illustration of the greater generality, we provide ... WebJun 12, 2024 · This perspective unifies multiple existing robust and stochastic optimization methods. We prove a theorem that generalizes the classical duality in the mathematical problem of moments. Enabled by this theorem, we reformulate the maximization with respect to measures in DRO into the dual program that searches for RKHS functions. WebIn this paper, we investigate a robust nonsmooth multiobjective optimization problem related to a multiobjective optimization with data uncertainty. We firstly introduce two kinds of generalized convex functions, which are not necessary to be convex. ... Finally, we obtain the weak, strong and converse robust duality results between the primal ... rainbow shops yonkers ny