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SPP 1962 - Nichtglatte Systeme und Komplementaritätsprobleme mit verteilten Parametern

Otto-von-Guericke-Universität Magdeburg

Universitätsplatz 2

39106 Magdeburg

http://mathopt.de/RESEARCH/

Many of the most challenging problems in the applied sciences involve non-differentiable structures as well as partial differential operators, thus leading to non-smooth distributed parameter systems. The non-smoothness considered in this DFG-Priority Program (DFG-PP) typically arises

(i) directly in the problem formulation,
(ii) through inequality constraints, nonlinear complementarity or switching systems, or
(iii) as a result of competition and hierarchy.

In fact, very challenging applications for (i) come from frictional contact problems, or non-smooth constitutive laws associated with physical processes such as Bean’s critical state model for the magnetization of superconductors, which leads to a quasi-variational inequality (QVI) problem; for (ii) are related to non-penetration conditions in contact problems, variational inequality problems, or inequality constraints in optimization problems which, upon proper re-formulation lead to complementarity problems and further, by means of non-linear complementarity problem (NCP) functions, to non-smooth systems similar to (i); and for (iii) come from multi-objective control systems or leader-follower principles, as they can be found in optimal system design in robotics and biomechanics. Modelling ”competition” often leads to generalized Nash equilibrium problems (GNEPs) or partial differential games. Moreover, modelling ”hierarchy” results in mathematical programs with equilibrium constraints (MPECs), a class of optimization problems with degenerate, non-smooth constraints. All of these problems are highly nonlinear, lead to QVIs, and represent rather novel mathematical structures in applications based on partial differential operators. In these and related applications, the transition from smoothing or simulation-based approaches to genuinely non-smooth techniques or to multi-objective respectively hierarchical optimization is crucial.