Abstract This paper provides some new results on robust approximate optimal solutions of a fractional semi-infinite optimization problem under uncertainty data in the constraint functions. By employing conjugate analysis and robust optimization approach (worst-case approach), we obtain some necessary and sufficient optimality conditions for robust approximate optimal solutions of such a fractional semi-infinite optimization problem. In addition, we state a mixed type approximate dual problem to the reference problem and obtain some robust duality properties between them. The results obtained in this paper improve the corresponding results in the literature
We propose an approach to address data uncertainty for discrete optimization problems that allows co...
summary:In this paper, the robust counterpart of the linear fractional programming problem under lin...
Robust and distributionally robust optimization are modeling paradigms for decision-making under unc...
This paper deals with robust quasi approximate optimal solutions for a nonsmooth semi-infinite optim...
We review our results for approximate solutions for a robust convex optimization problem with a geom...
In robust optimization, the general aim is to find a solution that performs well over a set of possi...
This article focuses on optimality conditions for a robust fractional interval-valued optimization p...
This paper deals with the robust strong duality for nonconvex optimization problem with the data unc...
Abstract This paper deals with convex optimization problems in the face of data uncertainty within t...
The multiobjective optimization model studied in this paper deals with simultaneous minimization of ...
Many combinatorial optimization problems arising in real-world applications do not have accurate est...
Abstract. We consider a rather general class of mathematical programming problems with data uncertai...
Robust optimization is a valuable alternative to stochastic programming, where all underlying probab...
© 2017 Springer-Verlag GmbH Germany In this paper, we study convex programming problems with data un...
In this paper, the robust counterpart of the linear fractional programming problem under linear ineq...
We propose an approach to address data uncertainty for discrete optimization problems that allows co...
summary:In this paper, the robust counterpart of the linear fractional programming problem under lin...
Robust and distributionally robust optimization are modeling paradigms for decision-making under unc...
This paper deals with robust quasi approximate optimal solutions for a nonsmooth semi-infinite optim...
We review our results for approximate solutions for a robust convex optimization problem with a geom...
In robust optimization, the general aim is to find a solution that performs well over a set of possi...
This article focuses on optimality conditions for a robust fractional interval-valued optimization p...
This paper deals with the robust strong duality for nonconvex optimization problem with the data unc...
Abstract This paper deals with convex optimization problems in the face of data uncertainty within t...
The multiobjective optimization model studied in this paper deals with simultaneous minimization of ...
Many combinatorial optimization problems arising in real-world applications do not have accurate est...
Abstract. We consider a rather general class of mathematical programming problems with data uncertai...
Robust optimization is a valuable alternative to stochastic programming, where all underlying probab...
© 2017 Springer-Verlag GmbH Germany In this paper, we study convex programming problems with data un...
In this paper, the robust counterpart of the linear fractional programming problem under linear ineq...
We propose an approach to address data uncertainty for discrete optimization problems that allows co...
summary:In this paper, the robust counterpart of the linear fractional programming problem under lin...
Robust and distributionally robust optimization are modeling paradigms for decision-making under unc...