The paper deals with optimization problems with uncertain constraints and linear perturbations of the objective function, which are associated with given families of perturbation functions whose dual variable depends on the uncertainty parameters. More in detail, the paper provides characterizations of stable strong robust duality and stable robust duality under convexity and closedness assumptions. The paper also reviews the classical Fenchel duality of the sum of two functions by considering a suitable family of perturbation functions.This research was supported by the Ministry of Economy and Competitiveness of Spain and the European Regional Development Fund (ERDF) of the European Commission, Project MTM2014-59179-C2-1-P, by the Australi...
© 2017 Springer-Verlag GmbH Germany In this paper, we study convex programming problems with data un...
Abstract. In this paper, Mond-Weir type duality results for a uncertain multiobjective robust optimi...
The multiobjective optimization model studied in this paper deals with simultaneous minimization of ...
The paper deals with optimization problems with uncertain constraints and linear perturbations of th...
The paper deals with optimization problems with uncertain constraints and linear perturbations of th...
This paper considers an uncertain convex optimization problem, posed in a locally convex decision sp...
This paper deals with the robust strong duality for nonconvex optimization problem with the data unc...
Robust optimization has come out to be a potent approach to study mathematical problems with data un...
Robust optimization has come out to be a potent approach to study mathematical problems with data un...
In this paper we present a robust conjugate duality theory for convex programming problems in the fa...
Robust and distributionally robust optimization are modeling paradigms for decision-making under unc...
We introduce a robust optimization model consisting in a family of perturbation functions giving ris...
This paper considers an uncertain convex optimization problem, posed in a locally convex decision sp...
This paper considers an uncertain convex optimization problem, posed in a locally convex decision sp...
In this paper we associate with an infinite family of real extended functions defined on a locally c...
© 2017 Springer-Verlag GmbH Germany In this paper, we study convex programming problems with data un...
Abstract. In this paper, Mond-Weir type duality results for a uncertain multiobjective robust optimi...
The multiobjective optimization model studied in this paper deals with simultaneous minimization of ...
The paper deals with optimization problems with uncertain constraints and linear perturbations of th...
The paper deals with optimization problems with uncertain constraints and linear perturbations of th...
This paper considers an uncertain convex optimization problem, posed in a locally convex decision sp...
This paper deals with the robust strong duality for nonconvex optimization problem with the data unc...
Robust optimization has come out to be a potent approach to study mathematical problems with data un...
Robust optimization has come out to be a potent approach to study mathematical problems with data un...
In this paper we present a robust conjugate duality theory for convex programming problems in the fa...
Robust and distributionally robust optimization are modeling paradigms for decision-making under unc...
We introduce a robust optimization model consisting in a family of perturbation functions giving ris...
This paper considers an uncertain convex optimization problem, posed in a locally convex decision sp...
This paper considers an uncertain convex optimization problem, posed in a locally convex decision sp...
In this paper we associate with an infinite family of real extended functions defined on a locally c...
© 2017 Springer-Verlag GmbH Germany In this paper, we study convex programming problems with data un...
Abstract. In this paper, Mond-Weir type duality results for a uncertain multiobjective robust optimi...
The multiobjective optimization model studied in this paper deals with simultaneous minimization of ...