no. 4
Robust duality for generalized convex nonsmooth vector programs with uncertain data in constraints
RAIRO. Operations Research, Tome 55 (2021) no. 4, pp. 2181-2188

Robust optimization has come out to be a potent approach to study mathematical problems with data uncertainty. We use robust optimization to study a nonsmooth nonconvex mathematical program over cones with data uncertainty containing generalized convex functions. We study sufficient optimality conditions for the problem. Then we construct its robust dual problem and provide appropriate duality theorems which show the relation between uncertainty problems and their corresponding robust dual problems.

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Accepté le :
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DOI : 10.1051/ro/2021095
Classification : 49J52, 90C26, 90C30, 90C46
Keywords: Robust nonsmooth optimization, robust duality, generalized convexity 
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     author = {Ahmad, Izhar and Kaur, Arshpreet and Sharma, Mahesh Kumar},
     title = {Robust duality for generalized convex nonsmooth vector programs with uncertain data in constraints},
     journal = {RAIRO. Operations Research},
     pages = {2181--2188},
     year = {2021},
     publisher = {EDP-Sciences},
     volume = {55},
     number = {4},
     doi = {10.1051/ro/2021095},
     mrnumber = {4284909},
     zbl = {1490.90206},
     language = {en},
     url = {https://www.numdam.org/articles/10.1051/ro/2021095/}
}
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Ahmad, Izhar; Kaur, Arshpreet; Sharma, Mahesh Kumar. Robust duality for generalized convex nonsmooth vector programs with uncertain data in constraints. RAIRO. Operations Research, Tome 55 (2021) no. 4, pp. 2181-2188. doi: 10.1051/ro/2021095

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