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Mathematical and Computer Modelling of Dynamical Systems
Methods, Tools and Applications in Engineering and Related Sciences
Volume 22, 2016 - Issue 4: Model Order Reduction
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Articles

An empirical interpolation approach to reduced basis approximations for variational inequalities

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Pages 345-361 | Received 10 Nov 2015, Accepted 02 Jun 2016, Published online: 23 Jun 2016
 

ABSTRACT

Variational inequalities (VIs) are pervasive in mathematical modelling of equilibrium and optimization problems in engineering and science. Examples of applications include traffic network equilibrium problems, financial equilibrium, obstacle problems, lubrication phenomena and many others. Since these problems are computationally expensive to solve, we focus here on the development of model order reduction techniques, in particular the reduced basis technique. Reduced basis techniques for the approximation of solutions to elliptic VIs have been developed in the last few years. These methods apply to VIs of the so-called first kind, i.e. problems that can be equivalently described by a minimization of a functional over a convex set. However, these recent approaches are inapplicable to VIs of the so-called second kind, i.e. problems that involve minimization of a functional containing non-differentiable terms. In this article, we evaluate the feasibility of using the reduced basis method (RBM) combined with the empirical interpolation method (EIM) to treat VIs. In the proposed approach, the problem is approximated using a penalty or barrier method, and EIM is then applied to the penalty or barrier term. Numerical examples are presented to assess the performance of the proposed method, in particular the accuracy and computational efficiency of the approximation. Although the numerical examples involve only VIs of the first kind, we also evaluate the feasibility of using the RBM combined with the EIM to treat VIs of the second kind.

AMS CLASSIFICATION:

Acknowledgments

We would like to acknowledge M. A. Grepl and M. Herty for fruitful discussions. Further, we are truly thankful to the anonymous referees for their comments and valuable feedback. This work was supported by the German Research Foundation through Grant GSC 111 and the Excellence Initiative of the German federal and state governments.

Disclosure statement

The authors have no potential conflicts of interest to disclose.

Additional information

Funding

This work was supported by the German Research Foundation through Grant GSC 111 and the Excellence Initiative of the German federal and state governments.

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