AbstractLeast squares solution of linear inequalities appears in many disciplines such as linear separability problems and inconsistency correction. In this paper we consider this problem with uncertainty in its data. Then we prove that its robust counterpart is equivalent to a second order conic linear optimization problem, which can be efficiently solved using interior point methods
This paper addresses the robust counterparts of optimization problems containing sums of maxima of l...
This paper adresses the robust counterparts of optimization problems containing sums of maxima of li...
Abstract. We treat uncertain linear programming problems by utilizing the notion of weighted ana-lyt...
AbstractLeast squares solution of linear inequalities appears in many disciplines such as linear sep...
Robust optimization is a rapidly developing methodology for handling optimization problems affected ...
Abstract: We propose a new way to derive tractable robust counterparts of a linear conic optimizatio...
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...
The solution of robust counterparts of optimization problems with uncertain data is currently attrac...
We show that the robust counterpart of a convex quadratic constraint with ellipsoidal implementation...
In this paper we provide a systematic way to construct the robust counterpart of a nonlinear uncerta...
Abstract: In this paper we provide a systematic way to construct the robust counterpart of a nonline...
Abstract This paper deals with convex optimization problems in the face of data uncertainty within t...
We review our results for approximate solutions for a robust convex optimization problem with a geom...
We derive computationally tractable formulations of the robust counterparts of convex quadratic and ...
This paper addresses the robust counterparts of optimization problems containing sums of maxima of l...
This paper adresses the robust counterparts of optimization problems containing sums of maxima of li...
Abstract. We treat uncertain linear programming problems by utilizing the notion of weighted ana-lyt...
AbstractLeast squares solution of linear inequalities appears in many disciplines such as linear sep...
Robust optimization is a rapidly developing methodology for handling optimization problems affected ...
Abstract: We propose a new way to derive tractable robust counterparts of a linear conic optimizatio...
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...
The solution of robust counterparts of optimization problems with uncertain data is currently attrac...
We show that the robust counterpart of a convex quadratic constraint with ellipsoidal implementation...
In this paper we provide a systematic way to construct the robust counterpart of a nonlinear uncerta...
Abstract: In this paper we provide a systematic way to construct the robust counterpart of a nonline...
Abstract This paper deals with convex optimization problems in the face of data uncertainty within t...
We review our results for approximate solutions for a robust convex optimization problem with a geom...
We derive computationally tractable formulations of the robust counterparts of convex quadratic and ...
This paper addresses the robust counterparts of optimization problems containing sums of maxima of l...
This paper adresses the robust counterparts of optimization problems containing sums of maxima of li...
Abstract. We treat uncertain linear programming problems by utilizing the notion of weighted ana-lyt...
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