Contributed Session Mon.1.H 3503

Monday, 10:30 - 12:00 h, Room: H 3503

Cluster 20: Robust optimization [...]

Extensions of robust optimization models

 

Chair: Frank Pfeuffer

 

 

Monday, 10:30 - 10:55 h, Room: H 3503, Talk 1

Michael Todd
A robust robust (sic) optimization result

Coauthor: Martina Gancarova

 

Abstract:
We study the loss in objective value obtained when an
inaccurate objective is optimized instead of the true one,
and show that "on average'', the loss incurred is very small,
for arbitrary compact feasible regions.

 

 

Monday, 11:00 - 11:25 h, Room: H 3503, Talk 2

Frank Pfeuffer
An extension of the controlled robustness model of Bertsimas and Sim

Coauthor: Utz-Uwe Haus

 

Abstract:
Realistic data in optimization models is often subject to uncertainty. Robust optimization models take such data uncertainty into account. Bertsimas and Sim proposed a robust model which deals with data uncertainty while allowing to control the amount of robustness in the problem by bounding the number of simultaneously uncertain coefficients. They showed that under this model robust min-cost-flow problems are solved by binary search using an oracle for min-cost-flow problems.
We extend this model by allowing more general means of imposing control on the amount of robustness via polyhedral control sets, which contain the model of Bertsimas and Sim as a special case. Under our model, robust min-cost-flow problems are solved by a subgradient approach using an oracle for min-cost-flow problems. Applying our approach to the restrictive control set of Bertsimas and Sim reduces the number of oracle calls needed by their approach by half.

 

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