This research first deals with a new comprehensive second order generalization to exponential type invexities, which encompasses most of the existing generalized sonvexity concepts (including [25] and [41]) in the literature, and then a wide range of parametric sufficient optimality conditions leading to the solvability for multiobjective fractional programming problems are established. These results are new and application-oriented to other fields of mathematical programming.

In this paper, we define and introduce some new concepts of the exponentially convex functions. We investigate several properties of the exponentially convex functions and discuss their relations with convex functions. Optimality conditions are characterized by a class of variational inequalities. Several interesting results characterizing the exponentially convex functions are obtained. Results obtained in this paper can be viewed as significant improvement of previously known results

In this basic research, the well-known error functions with the complex variable and some of their properties are first introduced, then some interesting results concerning error functions are presented. Finally, several conclusions and recommendations for the applications of error functions with complex argument are pointed out..

In this paper, we discuss some upper bounds for the spectral radius of block Hadamard product of block H-matrices. By using norm structure of block matrices, we establish estimations for the spectral radius.

Variational inequality and Complementarity have much in common, but there has been little direct contact between the researchers of these two related fields of mathematical sciences. Several problems arising from Fluid Mechanics, Solid Mechanics, Structural Engineering, Mathematical Physics, Geometry, Mathematical Programming etc. have the formulation of a Variational Inequality or Complementarity Problem.

In real-life applications there are some situations where the Decision Maker (DM) wishes to optimize multiple, and conflicting objective functions. This is known as Multi-Objective Programming (MOP) Problem. Stochastic programming is a branch of mathematical programming that deals with some situations in which an optimal decision is desired under some random parameters.