Presented By: Department of Mathematics
Optimality conditions in optimization under uncertainty
Christiane Tammer (Martin-Luther-University Halle-Wittenberg, Institute of Mathematics, Germany)
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.
Livestream Information
ZoomJanuary 27, 2023 (Friday) 9:00am
Meeting ID: 92332350184
Meeting Password: 123456
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