In this paper we study robust convex quadratically constrained programs, a subset of the class of robust convex programs introduced by Ben-Tal and Nemirovski [4]. Unlike [4], our focus in this paper is to identify uncertainty structures that allow the corresponding robust quadratically constrained programs to be reformulated as second-order cone programs. We propose three classes of uncertainty sets that satisfy this criterion and present examples where these classes of uncertainty sets are natural. 1 Problem formulation A generic quadratically constrained program (QCP) is defined as follows
This paper studies robust solutions and semidefinite linear programming (SDP) relaxations of a class...
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This paper deals with uncertain multi-objective convex programming problems, where the data of the o...
We derive computationally tractable formulations of the robust counterparts of convex quadratic and ...
We study robust convex quadratically constrained quadratic programs where the uncertain problem para...
Abstract This paper deals with convex optimization problems in the face of data uncertainty within t...
We treat in this paper Linear Programming (LP) problems with uncertain data. The focus is on uncerta...
We treat in this paper linear programming (LP) problems with uncertain data. The focus is on uncerta...
We propose a framework for robust modeling of linear programming problems using uncertainty sets des...
We show that the robust counterpart of a convex quadratic constraint with ellipsoidal implementation...
stability and robustness analysis problems to nondifferentiable convex programs. They have also prov...
© 2017 Springer-Verlag GmbH Germany In this paper, we study convex programming problems with data un...
In this paper, we examine stable exact relaxations for classes of parametric robust convex polynomia...
The radius of robust feasibility of a convex program with uncertain constraints gives a value for th...
Robust optimization has come out to be a potent approach to study mathematical problems with data un...
This paper studies robust solutions and semidefinite linear programming (SDP) relaxations of a class...
Abstract: We propose a new way to derive tractable robust counterparts of a linear conic optimizatio...
This paper deals with uncertain multi-objective convex programming problems, where the data of the o...
We derive computationally tractable formulations of the robust counterparts of convex quadratic and ...
We study robust convex quadratically constrained quadratic programs where the uncertain problem para...
Abstract This paper deals with convex optimization problems in the face of data uncertainty within t...
We treat in this paper Linear Programming (LP) problems with uncertain data. The focus is on uncerta...
We treat in this paper linear programming (LP) problems with uncertain data. The focus is on uncerta...
We propose a framework for robust modeling of linear programming problems using uncertainty sets des...
We show that the robust counterpart of a convex quadratic constraint with ellipsoidal implementation...
stability and robustness analysis problems to nondifferentiable convex programs. They have also prov...
© 2017 Springer-Verlag GmbH Germany In this paper, we study convex programming problems with data un...
In this paper, we examine stable exact relaxations for classes of parametric robust convex polynomia...
The radius of robust feasibility of a convex program with uncertain constraints gives a value for th...
Robust optimization has come out to be a potent approach to study mathematical problems with data un...
This paper studies robust solutions and semidefinite linear programming (SDP) relaxations of a class...
Abstract: We propose a new way to derive tractable robust counterparts of a linear conic optimizatio...
This paper deals with uncertain multi-objective convex programming problems, where the data of the o...