Efficient Solvers for Saddle Point Problems with Applications to PDE–Constrained Optimization

Part of the Lecture Notes in Applied and Computational Mechanics book series (LNACM, volume 66)

Abstract

We review some of the recent work on preconditioners for saddle point problems. In particular, we discuss preconditioners that are constructed based on exact or inexact Schur complements and on interpolation theory. These preconditioners are used within Krylov subspace methods, for which it is shown that the total number of iterations is bounded by global constants. The described techniques are applied to two model problems from optimal control.

Keywords

Condition Number Saddle Point Problem Interpolation Theory Krylov Subspace Method Element Space Versus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institut für Numerische MathematikJohannes Kepler Universität LinzLinzAustria

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