This paper is concerned with the Lipschitzian behavior of the optimal set of convex semi-infinite optimization problems under continuous perturbations of the right hand side of the constraints and linear perturbations of the objective function. In this framework we provide a sufficient condition for the metric regularity of the inverse of the optimal set mapping. This condition consists of the Slater constraint qualification, together with a certain additional requirement in the Karush-Kuhn-Tucker conditions. For linear problems this sufficient condition turns out to be also necessary for the metric regularity, and it is equivalent to some well-known stability concepts
International audienceIn this paper, we first provide counterexamples showing that sublevels of prox...
The original motivation for this paper was to provide an efficient quantitative analysis of convex i...
This paper is concerned with isolated calmness of the solution mapping of a parameterized convex sem...
This paper is concerned with the Lipschitzian behavior of the optimal set of convex semi-infinite op...
In this paper we make use of subdifferential calculus and other variational techniques, traced out f...
AbstractCertain stability concepts for local minimizers of nonlinear programs require, on the one ha...
AbstractWe aim to quantify the stability of systems of (possibly infinitely many) linear inequalitie...
We consider the parametric space of all the linear semi-infinite programming problems with constrain...
This paper concerns parameterized convex infinite (or semi-infinite) inequality systems whose decisi...
The paper develops a stability theory for the optimal value and the optimal set mapping of optimizat...
This paper concerns applications of advanced techniques of variational analysis and generalized diff...
This paper concerns applications of advanced techniques of variational analysis and generalized diff...
This paper primarily concerns the study of parametric problems of infinite and semi-infinite program...
We present a perturbation theory for finite dimensional optimization problems subject to abstract co...
This paper is focused on the stability of the optimal value, and its immediate repercussion on the s...
International audienceIn this paper, we first provide counterexamples showing that sublevels of prox...
The original motivation for this paper was to provide an efficient quantitative analysis of convex i...
This paper is concerned with isolated calmness of the solution mapping of a parameterized convex sem...
This paper is concerned with the Lipschitzian behavior of the optimal set of convex semi-infinite op...
In this paper we make use of subdifferential calculus and other variational techniques, traced out f...
AbstractCertain stability concepts for local minimizers of nonlinear programs require, on the one ha...
AbstractWe aim to quantify the stability of systems of (possibly infinitely many) linear inequalitie...
We consider the parametric space of all the linear semi-infinite programming problems with constrain...
This paper concerns parameterized convex infinite (or semi-infinite) inequality systems whose decisi...
The paper develops a stability theory for the optimal value and the optimal set mapping of optimizat...
This paper concerns applications of advanced techniques of variational analysis and generalized diff...
This paper concerns applications of advanced techniques of variational analysis and generalized diff...
This paper primarily concerns the study of parametric problems of infinite and semi-infinite program...
We present a perturbation theory for finite dimensional optimization problems subject to abstract co...
This paper is focused on the stability of the optimal value, and its immediate repercussion on the s...
International audienceIn this paper, we first provide counterexamples showing that sublevels of prox...
The original motivation for this paper was to provide an efficient quantitative analysis of convex i...
This paper is concerned with isolated calmness of the solution mapping of a parameterized convex sem...