AbstractIn this paper we present first and second order sufficient conditions for strict local minima of orders 1 and 2 to vector optimization problems with an arbitrary feasible set and a twice directionally differentiable objective function. With this aim, the notion of support function to a vector problem is introduced, in such a way that the scalar case and the multiobjective case, in particular, are contained. The obtained results extend the multiobjective ones to this case. Moreover, specializing to a feasible set defined by equality, inequality, and set constraints, first and second order sufficient conditions by means of Lagrange multiplier rules are established
In this paper, we define some new generalizations of strongly convex functions of order m for loc...
We consider unconstrained finite dimensional multi-criteria optimization problems, where the objecti...
For equality-constrained optimization problems with locally Lipschitzian objective functions, we der...
AbstractIn this paper we present first and second order sufficient conditions for strict local minim...
AbstractThe notion of strict minimum of order m for real optimization problems is extended to vector...
This paper deals with the relations between vector variational inequality problems and nonsmooth vec...
In this paper second-order necessary optimality conditions for nonsmooth vector optimization proble...
In this paper we suggest a general approach in studying optimality for a multiobjective problem. Fir...
AbstractIn this paper, a new approximation method is introduced to characterize a so-called vector s...
The aim of this lecture is to present the second-order necessary and suf-ficient conditions for vect...
The concept of lower limit for a real-valued function is extended to vector optimization; the vector...
In this paper we will establish some necessary and/or sufficient optimality conditions for a vector ...
For mappings defined on metric spaces with values in Banach spaces, the notions of derivative vecto...
In this paper a vector optimization problem (VOP) is considered where each component of objective an...
The paper is devoted to the existence of global optimal solutions for a general class of nonsmooth p...
In this paper, we define some new generalizations of strongly convex functions of order m for loc...
We consider unconstrained finite dimensional multi-criteria optimization problems, where the objecti...
For equality-constrained optimization problems with locally Lipschitzian objective functions, we der...
AbstractIn this paper we present first and second order sufficient conditions for strict local minim...
AbstractThe notion of strict minimum of order m for real optimization problems is extended to vector...
This paper deals with the relations between vector variational inequality problems and nonsmooth vec...
In this paper second-order necessary optimality conditions for nonsmooth vector optimization proble...
In this paper we suggest a general approach in studying optimality for a multiobjective problem. Fir...
AbstractIn this paper, a new approximation method is introduced to characterize a so-called vector s...
The aim of this lecture is to present the second-order necessary and suf-ficient conditions for vect...
The concept of lower limit for a real-valued function is extended to vector optimization; the vector...
In this paper we will establish some necessary and/or sufficient optimality conditions for a vector ...
For mappings defined on metric spaces with values in Banach spaces, the notions of derivative vecto...
In this paper a vector optimization problem (VOP) is considered where each component of objective an...
The paper is devoted to the existence of global optimal solutions for a general class of nonsmooth p...
In this paper, we define some new generalizations of strongly convex functions of order m for loc...
We consider unconstrained finite dimensional multi-criteria optimization problems, where the objecti...
For equality-constrained optimization problems with locally Lipschitzian objective functions, we der...