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There are lots of approaches to model uncertainty in optimization problems, for example, stochastic optimization and fuzzy optimization. While modeling practical problems in real world, it is observed that some parameters of the problem may not be known certainly. Specially, in an optimization problem it is possible that the parameters of the model be inexact.

Here we consider an optimization problem with interval valued objective function. Stancu, Minasian and Tigan [2,3], investigated this kind of optimization problem. Hsien-Chung Wu [4,5] proved and derived the Karush-Kuhn-Tucker (KKT) optimality conditions for an optimization problem with interval valued objective function.

While modeling practical problems in the real world, it is observed that some parameters of a problem may not be known precisely. For example, the parameters of the model in an optimization problem may be inexact. Several There are lots of approaches have been proposed for modeling uncertaintiesto model uncertainty in optimization problems, for example, stochastic optimization and fuzzy optimization. While modeling practical problems in real world, it is observed that some parameters of the problem may not be known certainly. Specially, in an optimization problem it is possible that the parameters of the model be inexact.

Here we consider an optimization problem with interval valued objective function.  Stancu, -Minasian and Tigan  [2,3], investigated this kind ofan optimization problem with an interval valued objective function. Further, Hsien-Chung Wu [4,5] proved and derived the Karush-Kuhn-Tucker (KKT) optimality conditions for an optimization problem with an interval valued objective function. Here, we consider an optimization problem with an interval valued objective function.

There are lots of approaches to model uncertainty in optimization problems, for example, stochastic optimization and fuzzy optimization. While modeling practical problems in real world, it is observed that some parameters of the problem may not be known certainly. Specially, in an optimization problem it is possible that the parameters of the model be inexact.

Here we consider an optimization problem with interval valued objective function. Stancu, Minasian and Tigan [2,3], investigated this kind of optimization problem. Hsien-Chung Wu [4,5] proved and derived the Karush-Kuhn-Tucker (KKT) optimality conditions for an optimization problem with interval valued objective function.

Several approaches are available forThere are lots of approaches to modeling uncertaintiesy in optimization problems, for example, stochastic optimization and fuzzy optimization. While modeling practical problems in the real world, it is observed that some parameters of the a problem may not be known certainlyprecisely. Specially,For example, the parameters of the model in an optimization problem it is possible that the parameters ofmay the model be inexact.

Here, we consider an optimization problem with an interval valued objective function. Stancu, Minasian, and Tigan [2,3], also investigated this kind of optimization problem. Further, Hsien-Chung Wu [4,5] proved and derived the Karush-Kuhn-Tucker (KKT) optimality conditions for an optimization problem with an interval valued objective function.

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