RESUMO
In this paper, we study robust necessary optimality conditions for a nondifferentiable complex fractional programming with uncertain data. A robust counterpart of uncertain complex fractional programming is introduced in the worst-case scenario. The concept of robust optimal solution of the uncertain complex fractional programming is introduced by using robust counterpart. We give an equivalence between the optimal solutions of the robust counterpart and a minimax nonfractional parametric programming. Finally, Fritz John-type and Karush-Kuhn-Tucker-type robust necessary optimality conditions of the uncertain complex fractional programming are established under some suitable conditions.
RESUMO
Accurate estimation of the in vivo locations of skeletal landmarks plays an integral role in several biomechanical research techniques. Because of rounding errors caused by instruments or skin movement, the data obtained through cinematography are usually not accurate and rise to a distance matrix which, because of the data errors, may not be Euclidean. The aim of this paper is to find the best Euclidean distance matrix (EDM) that approximates the distance matrix and then, an accurate estimation of the locations of skeletal landmarks. A useful scheme for parametrizing an orthogonal matrix is also described.
