Thursday, 14:15 - 14:40 h, Room: MA 004


Marc Goerigk
A geometric approach to recovery robustness

Coauthors: Emilio Carrizosa, Anita Schöbel


Finding robust solutions of an optimization problem is an important issue in practice, as solutions to optimization problems may become infeasible if the exact model parameters are not known exactly. Roughly speaking, the goal in robust optimization is to find solutions which are still valid if the input data changes, thus increasing the practical applicability of optimization algorithms in real-world problems.
Various concepts on how to define robustness have been suggested. A recent model follows the idea of recovery robustness. Here, one looks for a first-stage solution which is recoverable to a feasible one for any possible scenario in the second stage. Unfortunately, finding recovery robust solutions is in many cases computationally hard.
In this talk we propose the concept of "recovery to feasibility'', a variation of recovery robustness based on geometric ideas, that is applicable for a wide range of problems. In particular, an optimal solution can be determined efficiently for linear programming problems and problems with quasiconvex constraints for different types of uncertainties. For more complex settings reduction approaches are proposed.


Talk 3 of the invited session Thu.2.MA 004
"Multistage robustness" [...]
Cluster 20
"Robust optimization" [...]


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