Robust Preconditioners for Saddle Point Problems

  • Owe Axelsson
  • Maya Neytcheva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2542)


We survey preconditioning methods for matrices on saddle point form, as typically arising in constrained optimization problems. Special consideration is given to indefinite matrix preconditioners and a preconditioner which results in a symmetric positive definite matrix, which latter may enable the use of the standard conjugate gradient (CG) method. These methods result in eigenvalues with positive real parts and small or zero imaginary parts. The behaviour of some of these techniques is illustrated on solving a regularized Stokes problem.


Conjugate Gradient Method Preconditioned Conjugate Gradient Saddle Point Problem Unit Number Numerical Linear Algebra 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Owe Axelsson
    • 1
  • Maya Neytcheva
    • 2
  1. 1.Department of MathematicsUniversity of NijmegenED NijmegenThe Netherlands
  2. 2.Department of Scientific ComputingUppsala UniversityUppsalaSweden

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