Ergodic Approach for Nonconvex Robust Optimization Problems
Pérez-Aros, Pedro
arXiv, Tome 2019 (2019) no. 0, / Harvested from
In this work we show the consistency of an approach for solving robust optimization problems using sequences of sub-problems generated by erogodic measure preserving transformations. The main result of this paper is that the minimizers and the optimal value of the sub-problems converge, in some sense, to the minimizers and the optimal value of the initial problem, respectively. Our result particularly implies the consistency of the \emph{scenario approach} for nonconvex optimization problems. Finally, we show that our method can be used to solve infinite programming problems.
Publié le : 2019-02-26
Classification:  Mathematics - Optimization and Control,  90C15, 90C26, 90C90, 60B11 
@article{1902.10325,
     author = {P\'erez-Aros, Pedro},
     title = {Ergodic Approach for Nonconvex Robust Optimization Problems},
     journal = {arXiv},
     volume = {2019},
     number = {0},
     year = {2019},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1902.10325}
}

Pérez-Aros, Pedro. Ergodic Approach for Nonconvex Robust Optimization Problems. arXiv, Tome 2019 (2019) no. 0, . http://gdmltest.u-ga.fr/item/1902.10325/