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Optimality conditions in optimization under uncertainty

Christiane Tammer (Martin-Luther-University Halle-Wittenberg, Institute of Mathematics, Germany)
Friday, January 27, 2023
9:00-10:00 AM
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
Location: Virtual
Event Link:
Event Password: 123456
Event Type: Workshop / Seminar
Tags: Mathematics
Source: Happening @ Michigan from Department of Mathematics, Variational Analysis and Optimization Seminar - Department of Mathematics