Stochastic optimization problems with second order stochastic dominance constraints via Wasserstein metric

0502907 - ÚTIA 2019 RIV CZ eng J - Článek v odborném periodiku
Kaňková, Vlasta - Omelchenko, Vadym
Stochastic optimization problems with second order stochastic dominance constraints via Wasserstein metric.
Kybernetika. Roč. 54, č. 6 (2018), s. 1231-1246. ISSN 0023-5954.
[19th Joint Czech -German-Slovak Conference on Mathematical Methods in Economy and Industry (MMEI). Jindřichův Hradec, 04.06.2018-06.06.2018]
Grant CEP: GA ČR GA18-02739S
Institucionální podpora: RVO:67985556
Klíčová slova: Stochastic programming problems * Second order stochastic dominance constraints * Wasserstein metric * Stability * Relaxation * Scenario generation * Empirical estimates * Light-and heavy tailed distributions * Crossing
Obor OECD: Statistics and probability
Impakt faktor: 0.560, rok: 2018
http://library.utia.cas.cz/separaty/2019/E/kankova-0502907.pdf

Optimization problems with stochastic dominance constraints are helpful to many real-life applications. We can recall e.g. problems of portfolio selection or problems connected with energy production. The above mentioned constraints are very suitable because they guarantee a solution fulfilling partial order between utility functions in a given subsystem U of the utility functions. Especially, considering U = U_1 (where U_ is a system of a non decreasing concave nonnegative utility functions) we obtain second order stochastic dominance constraints. Unfortunately it is also known that these problems are rather complicated as from the theoretical and the numerical point of view. Moreover, these problems go to semi-infinite optimization problems for which Slater's condition is not necessary fulfilled. Consequently it is suitable to modify the constraints. A question arises how to do it. The aim of the paper is to suggest one of the possibilities how to modify the original problem with an „estimation“ of a gap between the original and modified problem. To this end the stability results obtained on the base of the Wasserstein metric corresponding to L_1 norm are employed. Moreover, we mention a scenario generation and an investigation of empirical estimates. At the end attention will be paid to heavy tailed distributions.
Trvalý link: http://hdl.handle.net/11104/0295272