Miguel Sama

On solutions of set-valued optimization problems

We consider two criteria of a solution associated with a set-valued optimization problem, a vector criterion and a set criterion. We show how solutions of a vector type can help to find solutions of a set type and reciprocally. As an application, we obtain a sufficient condition for the existence of solutions of a set type via vector optimization.

Some equivalent problems in set optimization

Given a set-valued optimization problem (P), there is more than one way of defining the solutions associated with it. Depending on the decision makers preference, we consider the vector criterion or the set criterion. Both criteria of solution are considered together to solve problem (P) by reducing the feasible set.

Scalar multiplier rules in set-valued optimization

In this work we establish necessary and sufficient conditions for weak minimizers of a constrained set-valued optimization problem. These conditions are given by means of multiplier rules formulated in terms of contingent epiderivatives of scalar set-valued maps. We consider that the image spaces are finite dimensional and the set-valued maps are convex and stable. For these conditions, our results provide a scalar version of analogous existing results in the literature under weaker existence conditions. Moreover we prove that the multiplier rules can be computed in terms of the directional derivative of associated maps of infima.

Scalar Lagrange Multiplier Rules for Set-Valued Problems in Infinite-Dimensional Spaces

This paper deals with Lagrange multiplier rules for constrained set-valued optimization problems in infinite-dimensional spaces, where the multipliers appear as scalarization functions of the maps instead of the derivatives. These rules provide necessary conditions for weak minimizers under hypotheses of stability, convexity, and directional compactness. Counterexamples show that the hypotheses are minimal.

An extension of the Basic Constraint Qualification to nonconvex vector optimization problems

In this paper a Basic Constraint Qualification is introduced for a nonconvex infinite-dimensional vector optimization problem extending the usual one from convex programming assuming the Hadamard differentiability of the maps. Corresponding KKT conditions are established by considering a decoupling of the constraint cone into half-spaces. This extension leads to generalized KKT conditions which are finer than the usual abstract multiplier rule. A second constraint qualification expressed directly in terms of the data is also introduced, which allows us to compute the contingent cone to the feasible set and, as a consequence, it is proven that this condition is a particular case of the first one. Relationship with other constraint qualifications in infinite-dimensional vector optimization, specially with the Kurcyuscz-Robinson-Zowe constraint qualification, are also given.

About Hahn-Banach extension theorems and applications to set-valued optimization

In this paper we revise several generalizations of the algebraic Hanh-Banach extension theorem for set-valued maps and give new versions in terms of set-relations and selections. We apply our results by establishing optimality conditions for a set-valued constrained optimization problem considering several types of solutions. Finally, we give necessary conditions for the existence of affine selections via affinelike maps in the framework of linear spaces.