Advances in Computational Mathematics

, 35:149

A preconditioning technique for a class of PDE-constrained optimization problems

Authors

    • Department of Mathematics and Computer ScienceEmory University
  • Eldad Haber
    • Department of Mathematics and Computer ScienceEmory University
    • Department of MathematicsUniversity of British Columbia
  • Lauren Taralli
    • Quantitative Analytics Research GroupStandard & Poor’s
Article

DOI: 10.1007/s10444-011-9173-8

Cite this article as:
Benzi, M., Haber, E. & Taralli, L. Adv Comput Math (2011) 35: 149. doi:10.1007/s10444-011-9173-8

Abstract

We investigate the use of a preconditioning technique for solving linear systems of saddle point type arising from the application of an inexact Gauss–Newton scheme to PDE-constrained optimization problems with a hyperbolic constraint. The preconditioner is of block triangular form and involves diagonal perturbations of the (approximate) Hessian to insure nonsingularity and an approximate Schur complement. We establish some properties of the preconditioned saddle point systems and we present the results of numerical experiments illustrating the performance of the preconditioner on a model problem motivated by image registration.

Keywords

Constrained optimizationKKT conditionsSaddle point problemsHyperbolic PDEsKrylov subspace methodsPreconditioningMonge–Kantorovich problemImage registration

Mathematics Subject Classifications (2010)

65F0865F2249M0549M1590C30
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Copyright information

© Springer Science+Business Media, LLC. 2011