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The working goup for Applied Optimization is concerned with the development of optimality conditions and solution algorithms for optimization problems given in terms of nonsmooth data in order to address applications in data science, optimal control of differential equations, and machine learning. We construct, study, and appl novel generalized derivatives for nonsmooth mappings which are beneficial in numerical optimization from a theoretical and practical point of view.
We offer diverse lectures addressing different aspects of mathematical optimization as well as math courses for studients of other departments.