Thursday, 14:15 - 14:40 h, Room: MA 144


Alois Pichler
Approximation of stochastic processes


We deal with extremely large scale and high dimensional optimization, where managerial decisions are allowed at consecutive instants of time. Scenarios, reflecting future states of the world, are considered random. It is well known how to deal with these types of stochastic optimization problems with an expectation in the objective, but we want to additionally address risk.
The newly introduced notion of a process distance (Pflug) allows quantifying approximations. We address approximations, which allow reasonable computation times and give viable bounds in comparison to the original problem. The results are general enough to involve risk measures, which (historically) appeared first in finance and insurance.
Finally the approximating processes can be improved by different means to improve their approximating quality


Talk 3 of the contributed session Thu.2.MA 144
"Large-scale and multi-stage stochastic optimization" [...]
Cluster 22
"Stochastic optimization" [...]


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