Domain decomposition and model reduction for the numerical solution of PDE constrained optimization problems with localized optimization variables

We introduce a technique for the dimension reduction of a class of PDE constrained optimization problems governed by linear time dependent advection diffusion equations for which the optimization variables are related to spatially localized quantities. Our approach uses domain decomposition applied...

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Veröffentlicht in:Computing and visualization in science Jg. 13; H. 6; S. 249 - 264
Hauptverfasser: Antil, Harbir, Heinkenschloss, Matthias, Hoppe, Ronald H. W., Sorensen, Danny C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer-Verlag 01.08.2010
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ISSN:1432-9360, 1433-0369
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Abstract We introduce a technique for the dimension reduction of a class of PDE constrained optimization problems governed by linear time dependent advection diffusion equations for which the optimization variables are related to spatially localized quantities. Our approach uses domain decomposition applied to the optimality system to isolate the subsystem that explicitly depends on the optimization variables from the remaining linear optimality subsystem. We apply balanced truncation model reduction to the linear optimality subsystem. The resulting coupled reduced optimality system can be interpreted as the optimality system of a reduced optimization problem. We derive estimates for the error between the solution of the original optimization problem and the solution of the reduced problem. The approach is demonstrated numerically on an optimal control problem and on a shape optimization problem.
AbstractList We introduce a technique for the dimension reduction of a class of PDE constrained optimization problems governed by linear time dependent advection diffusion equations for which the optimization variables are related to spatially localized quantities. Our approach uses domain decomposition applied to the optimality system to isolate the subsystem that explicitly depends on the optimization variables from the remaining linear optimality subsystem. We apply balanced truncation model reduction to the linear optimality subsystem. The resulting coupled reduced optimality system can be interpreted as the optimality system of a reduced optimization problem. We derive estimates for the error between the solution of the original optimization problem and the solution of the reduced problem. The approach is demonstrated numerically on an optimal control problem and on a shape optimization problem.
Author Sorensen, Danny C.
Hoppe, Ronald H. W.
Heinkenschloss, Matthias
Antil, Harbir
Author_xml – sequence: 1
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  surname: Antil
  fullname: Antil, Harbir
  organization: Department of Computational and Applied Mathematics, MS-134, Rice University
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  givenname: Matthias
  surname: Heinkenschloss
  fullname: Heinkenschloss, Matthias
  email: heinken@rice.edu
  organization: Department of Computational and Applied Mathematics, MS-134, Rice University
– sequence: 3
  givenname: Ronald H. W.
  surname: Hoppe
  fullname: Hoppe, Ronald H. W.
  organization: Department of Mathematics, University of Houston, Institute of Mathematics, University of Augsburg
– sequence: 4
  givenname: Danny C.
  surname: Sorensen
  fullname: Sorensen, Danny C.
  organization: Department of Computational and Applied Mathematics, MS-134, Rice University
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Issue 6
Keywords Model reduction
Domain decomposition
Optimal control
Shape optimization
Geometrical shape
Software development
Error estimation
Linear time
Advection diffusion equation
Substructure
Modeling
Constrained optimization
Truncation
Time dependence
Dimension reduction
Reduced order systems
Coupling
Optimal control (mathematics)
Mathematical programming
Language English
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Snippet We introduce a technique for the dimension reduction of a class of PDE constrained optimization problems governed by linear time dependent advection diffusion...
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Title Domain decomposition and model reduction for the numerical solution of PDE constrained optimization problems with localized optimization variables
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Volume 13
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