Externally Funded Project

Approximation of optimal control problems governed by PDEs

FWF Project P18090-N12
Runtime: 01.08.2005–30.09.2008

Project Team
Project Abstract

Many technical processes are described by systems of partial differential equations. Optimization of such processes or identification of process parameters lead to optimal control problems for partial differential equations. Usually, such problems are characterized by additional constraints, i.e. some quantities of the process have to fulfil certain equations and inequalities. This project is concerned with linear-quadratic optimal control problems: The optimization goal is a quadratic function of the process quantities. Moreover, these quantities occur linear in the equations and inequalities. This project is especially interested in investigating elliptic and parabolic differential equations.
Although this class of problems has a simple structure, it is impossible to solve such problems exactly. Therefore, it is necessary to discretize such problems in a suitable manner. Consequently, approximation properties of the discretized problems with respect to the solution of the continuous problem represent a main focus of the project.
There is a large progress in the theory of control constrained problems in the recent years. In contrast to this, approximation results for state constrained optimal control problems are nearly unknown. This project will lower the large gap between the well investigated control constrained case and the widely unknown field of state constrained problems.
Moreover, the results should be used to construct stopping criteria for iterative methods. Stopping criteria based on error estimates can drastically reduce computational time for optimal control problems in a reliable way. Therefore, it is possible to solve larger and more complicate problems in future.
So-called superconvergence effects appear by solving optimal control problems numerically. A better understanding of superconvergence effects should help to exploit them in numerical algorithms. Such algorithms deliver essential better numerical results for a given discretization.

Keywords and AMS Classification
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