Considering a general vector optimization problem, we attach to it by means of perturbation theory new vector duals. When the primal problem and the perturbation function are particularized different vector dual problems are obtained. In the special case of a constrained vector optimization problem the classical Wolfe and Mond-Weir duals to the latter, respectively, can be obtained from the gen...

The generalized methodology for electronic networks optimization was elaborated by means of the optimal control theory approach. A special control vector is defined to redistribute the compute expense between a network analysis and a parametric optimization. In this case the problem of the electronic system optimization is formulated as a classical problem of functional minimization of the opti...

In this paper, by virtue of lower Studniarski derivatives of set-valued maps, relationships between lower Studniarski derivative of a set-valued map and its profile map are discussed. Some results concerning sensitivity analysis are obtained in parametrized vector optimization.

This paper is concerned with generalized second-order contingent epiderivatives of frontier and solution maps in parametric vector optimization problems. Under some mild conditions, we obtain some formulas for computing generalized second-order contingent epiderivatives of frontier and solution maps, respectively. We also give some examples to illustrate the corresponding results.

In undergoing this life, many people always try to do and get the best. New knowledge, experience, lesson, and everything that can improve the life will be done. However, many people sometimes feel confused to get those things. Feeling the limited of experience and sources to be better is one of the lacks to own. However, there is a very simple thing that can be done. This is what your teacher ...

The concept of d−invexity for functions defined on Riemannian manifolds is introduced. Then, this notion is used to obtain optimality conditions for vector optimization problems on Riemannian manifolds. M.S.C. 2010: 58E17, 90C26.

Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter. Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome ...