Optimality conditions in optimization under uncertainty
Christiane Tammer (Martin-Luther-University Halle-Wittenberg, Institute of Mathematics, Germany)
Friday, January 27, 2023
Most optimization problems involve uncertain data due to measurement errors, unknown future developments and modeling approximations. Stochastic optimization assumes that the uncertain parameter is probabilistic. An other approach is called robust optimization which expects the uncertain parameter to belong to a set that is known prior. In this talk, we consider scalar optimization problems under uncertainty with infinite scenario sets. We apply methods from vector optimization in general spaces, set-valued optimization and scalarization techniques to derive necessary optimality conditions for solutions of robust optimization problems.
|Building:||Off Campus Location|
|Event Type:||Workshop / Seminar|
|Source:||Happening @ Michigan from Department of Mathematics, Variational Analysis and Optimization Seminar - Department of Mathematics|