In this article, we are concerned with a nondifferentiable minimax fractional programming problem. We derive the sufficient condition for an optimal solution to the problem and then establish weak, strong, and strict converse duality theorems for the problem and its dual problem under B-(p, r)-invexity assumptions. Examples are given to show that B-(p, r)-invex functions are generalization of (p, r)-invex and convex functions

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This paper is concerned with a pair of second-order mixed symmetric dual programs involving nondifferentiable functions. Weak, strong, and converse duality theorems are proved for aforementioned pair using the notion of second-orderF-convexity/pseudoconvexity assumptions.

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In this paper, a pair of Wolfe type second-order multiobjective symmetric dual programs involving nondifferentiable functions is formulated. Weak, strong and converse duality theorems are then established using the notion of second-order F-convexity assumptions. An example which is second-order F-convex but not convex is also illustrated. Further, special cases are discussed to show that this paper extends some known results of the literature.