We consider the linear programming problem with uncertainty set described by p,w-norm. We suggest that the robust counterpart of this problem is equivalent to a computationally convex optimization problem. We provide probabilistic guarantees on the feasibility of an optimal robust solution when the uncertain coefficients obey independent and identically distributed normal distributions
Robust optimization is a rapidly developing methodology for handling optimization problems affected ...
Abstract In this paper, we consider the multiobjective linear programs where coefficients in the obj...
Most research in robust optimization has so far been focused on inequality-only, convex conic progra...
We propose a framework for robust modeling of linear programming problems using uncertainty sets des...
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...
In this paper, we focus on a linear optimization problem with uncertainties, having expectations in ...
Abstract. We consider a rather general class of mathematical programming problems with data uncertai...
This thesis discusses different methods for robust optimization problems that are convex in the unce...
In this paper, we focus on a linear optimization problem with uncertainties, having expectations in ...
© 2017 Springer-Verlag GmbH Germany In this paper, we study convex programming problems with data un...
We derive computationally tractable formulations of the robust counterparts of convex quadratic and ...
Abstract In this paper, we consider the robust portfolio selection problem which has a data uncertai...
In this paper, we focus on a linear optimization problem with uncertainties, having expectations in ...
Abstract This paper deals with convex optimization problems in the face of data uncertainty within t...
Robust optimization is a rapidly developing methodology for handling optimization problems affected ...
Abstract In this paper, we consider the multiobjective linear programs where coefficients in the obj...
Most research in robust optimization has so far been focused on inequality-only, convex conic progra...
We propose a framework for robust modeling of linear programming problems using uncertainty sets des...
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...
In this paper, we focus on a linear optimization problem with uncertainties, having expectations in ...
Abstract. We consider a rather general class of mathematical programming problems with data uncertai...
This thesis discusses different methods for robust optimization problems that are convex in the unce...
In this paper, we focus on a linear optimization problem with uncertainties, having expectations in ...
© 2017 Springer-Verlag GmbH Germany In this paper, we study convex programming problems with data un...
We derive computationally tractable formulations of the robust counterparts of convex quadratic and ...
Abstract In this paper, we consider the robust portfolio selection problem which has a data uncertai...
In this paper, we focus on a linear optimization problem with uncertainties, having expectations in ...
Abstract This paper deals with convex optimization problems in the face of data uncertainty within t...
Robust optimization is a rapidly developing methodology for handling optimization problems affected ...
Abstract In this paper, we consider the multiobjective linear programs where coefficients in the obj...
Most research in robust optimization has so far been focused on inequality-only, convex conic progra...
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