An adaptive Newton algorithm for optimal control problems with application to optimal electrode design
In this work, we present an adaptive Newton-type method to solve nonlinear constrained optimization problems, in which the constraint is a system of partial differential equations discretized by the finite element method. The adaptive strategy is based on a goal-oriented a posteriori error estimatio...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
20 February 2018
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| In: |
Journal of optimization theory and applications
Year: 2018, Jahrgang: 177, Heft: 2, Pages: 498-534 |
| ISSN: | 1573-2878 |
| DOI: | 10.1007/s10957-018-1242-4 |
| Online-Zugang: | Resolving-System, Volltext: http://dx.doi.org/10.1007/s10957-018-1242-4 Verlag, Volltext: https://link.springer.com/article/10.1007/s10957-018-1242-4 
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| Verfasserangaben: | Thomas Carraro, Simon Dörsam, Stefan Frei, Daniel Schwarz |
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| 520 | |a In this work, we present an adaptive Newton-type method to solve nonlinear constrained optimization problems, in which the constraint is a system of partial differential equations discretized by the finite element method. The adaptive strategy is based on a goal-oriented a posteriori error estimation for the discretization and for the iteration error. The iteration error stems from an inexact solution of the nonlinear system of first-order optimality conditions by the Newton-type method. This strategy allows one to balance the two errors and to derive effective stopping criteria for the Newton iterations. The algorithm proceeds with the search of the optimal point on coarse grids, which are refined only if the discretization error becomes dominant. Using computable error indicators, the mesh is refined locally leading to a highly efficient solution process. The performance of the algorithm is shown with several examples and in particular with an application in the neurosciences: the optimal electrode design for the study of neuronal networks. | ||
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