Bilevel Optimal Control: Theory, Algorithms, and Applications
Description
The main goal of this project is to deepen the knowledge of bilevel optimal control problems (BOCPs) and infinitedimensional mathematical programs with complementarity constraints (MPCCs). In particular, we aim for theoretical results such as the development of new constraint qualifications in infinite dimensions and the sharpening of available optimality conditions. Moreover, we investigate algorithmic approaches which exploit the specific problem structures and study prototypical applications.
Our project consists of three work areas.
Theory of BOCPs (A): In this work area, we want to advance available optimization theory in infinite dimensions. In particular, we address optimality conditions of first and second order for MPCCs and aim for the construction of new constraint qualifications. Moreover, a discretization of infinitedimensional problems is unavoidable for numerical computations so we will also focus on results in numerical analysis.
Solution algorithms (B): On the one hand, we consider BOCPs where the lower level parametric optimization problem is an optimal control problem with a PDE constraint. Under some assumptions it is possible to exploit the lower level optimal value function in order to construct solution algorithms. In case where the overall problem data is fully convex while the upper level decision variable comes from a finitedimensional space, a global solution method is already available which exploits a piecewise affine upper approximate of the value function. Now, we want to tackle slightly different settings using related ideas. On the other hand, we aim for the development of an active set method for the numerical solution of MPCCs in the finitedimensional setting. Afterwards, a generalization to the function space setting will be discussed.
Prototypical applications (C): In this work area, some popular examples for BOCPs will be investigated. In particular, applications like the optimal measuring via a bilevel approach or parameter identification in optimal control problems are under consideration. We will apply the findings of work packages (A) and (B) in order to derive new optimality conditions and efficient solution algorithms.
Publications
No publications from this project yet.
Preprints
Yu Deng, Patrick Mehlitz, Uwe Prüfert: Coupled versus Decoupled Penalization of Control Complementarity Constraints (SPP1962125, 10/2019, [bib])
Research Area
Modeling, problem analysis, algorithm design and convergence analysis
The focus of this area is on the development and analysis of genuinely nonsmooth models in the sciences in order to properly capture realworld effects and to avoid comprising smoothing approaches. In simulation and optimization this requires to advance setvalued analysis and the design of robust algorithms for nonsmooth problems.Realization of algorithms, adaptive discretization and model reduction
As the target applications of this SPP involve nonsmooth structures and partial differential operators, the discretization of the associated problems and robust error estimation are important issues to be address, and proper modelreduction techniques need to be developed.Members

Prof. Stephan Dempe
Technische Universität Bergakademie Freiberg
Principal Investigator 
Prof. Gerd Wachsmuth
Brandenburgische Technische Universität CottbusSenftenberg
Principal Investigator 
Dr. Patrick Mehlitz
Brandenburgische Technische Universität CottbusSenftenberg
CoPI 
Uwe Prüfert
Technische Universität Bergakademie Freiberg
CoPI 
Felix Harder
Brandenburgische Technische Universität CottbusSenftenberg
Research Assistant
Project Related News

Oct 25, 2019 : New preprint submitted
Uwe Prüfert submitted the preprint SPP1962125 Coupled versus Decoupled Penalization of Control Complementarity Constraints.