A study on optimality and duality theorems of nonlinear generalized disjunctive fractional programming
This paper is concerned with the study of necessary and sufficient optimality conditions for convex - concave generalized fractional disjunctive programming problems for which the decision set is the union of a family of convex sets. The Lagrangian function for such problems is defined and the Kuhn - Tucker Saddle and Stationary points are characterized. In addition, some important theorems related to the Kuhn - Tucker problem for saddle and stationary points are established. Moreover, a general dual problem is formulated and weak, strong and converse duality theorems are proved. Throughout the presented paper illustrative examples are given to clarify and implement the developed theory. (C) 2007 Elsevier Ltd. All rights reserved.
optimality duality convexity concavity disjunctive programming generalized fractional programming