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ON NONSMOOTH OPTIMALITY THEOREMS FOR ROBUST OPTIMIZATION PROBLEMS
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 Title & Authors
ON NONSMOOTH OPTIMALITY THEOREMS FOR ROBUST OPTIMIZATION PROBLEMS
Lee, Gue Myung; Son, Pham Tien;
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 Abstract
In this paper, we prove a necessary optimality theorem for a nonsmooth optimization problem in the face of data uncertainty, which is called a robust optimization problem. Recently, the robust optimization problems have been intensively studied by many authors. Moreover, we give examples showing that the convexity of the uncertain sets and the concavity of the constraint functions are essential in the optimality theorem. We present an example illustrating that our main assumptions in the optimality theorem can be weakened.
 Keywords
robust optimization problem;Lagrange multipliers;locally Lipschitz functions;generalized gradients;necessary optimality conditions;
 Language
English
 Cited by
1.
On nonsmooth robust multiobjective optimization under generalized convexity with applications to portfolio optimization, European Journal of Operational Research, 2017  crossref(new windwow)
2.
Some characterizations of robust optimal solutions for uncertain convex optimization problems, Optimization Letters, 2016, 10, 7, 1463  crossref(new windwow)
3.
Optimality and duality for robust multiobjective optimization problems, Nonlinear Analysis: Theory, Methods & Applications, 2016, 134, 127  crossref(new windwow)
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