Saddle point problems arise frequently in many applications in science and engineering, including constrained optimization, mixed finite element formulations of partial differential equations, circuit analysis, and so forth. Indeed the formulation of most problems with constraints gives rise to saddle point systems. This paper provides a concise overview of iterative approaches for the solution of such systems which are of particular importance in the context of large scale computation. In particular we describe some of the most useful preconditioning techniques for Krylov subspace solvers applied to saddle point problems, including block and constraint preconditioners.
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References
M. Arioli, J. Mariška, M. Rozložník and M. T˚uma, Dual variable methods for mixed-hybrid finite element approximation of the potential fluid flow problem in porous media, Electr. Trans. Numer. Anal., 22 (2006), pp. 17–40.
M. Benzi and G. H. Golub, A preconditioner for generalized saddle point problems, SIAM J. Matrix Anal. Appl., 26 (2004), pp. 20–41.
M. Benzi, G. H. Golub, and J. Liesen, Numerical solution of saddle point problems, Acta Numerica, 14 (2005), pp. 1–137.
M. Benzi and M. A. Olshanskii, An augmented Lagrangian approach to the Oseen problem, SIAM J. Sci. Comput., 28 (2006), pp. 2095–2113.
M. Benzi and V. Simoncini, On the eigenvalues of a class of saddle point matrices, Numer. Math., 103 (2006), pp. 173–196.
P. Bochev and R. B. Lehoucq, Regularization and stabilization of discrete saddle-point variational problems, Electr. Trans. Numer. Anal., 22 (2006), pp. 97–113.
D. Braess, Finite Elements. Theory, Fast Solvers, and Applications in Solid Mechanics. Second Edition, Cambridge University Press, 2001.
H. S. Dollar, Iterative Linear Algebra for Constrained Optimization, DPhil (PhD) thesis, Oxford University Computing Laboratory, 2005.
H. S. Dollar, N. I. M. Gould and A. J. Wathen, On implicit-factorization constraint preconditioners, in ‘Large Scale Nonlinear Optimization’, Eds. G. Di Pillo, Gianni and M. Roma, Springer Verlag, Heidelberg, Berlin, New York (2006), pp. 61-82.
H. S. Dollar, N. I. M. Gould, W. H. A. Schilders and A. J. Wathen, Implicit-factorization preconditioning and iterative solvers for regularized saddle-point systems, SIAM J. Matrix Anal. Appl., 28 (2006), pp. 170–189.
H. S. Dollar, N. I. M. Gould, W. H. A. Schilders and A. J. Wathen, Using constraint preconditioners with regularized saddle-point problems, Comput. Optim. Appl., to appear.
H. S. Dollar and A. J. Wathen, Approximate factorization constraint preconditioners for saddle-point matrices, SIAM J. Sci. Comput., 27 (2006), pp. 1555–1572.
H. C. Elman, D. J. Silvester and A. J. Wathen, Finite Elements and Fast Iterative Solvers with Applications in Incompressible Fluid Dynamics, Numerical Mathematics and Scientific Computation, Oxford University Press, Oxford, UK, 2005.
B. Fischer, Polynomial Based Iteration Methods for Symmetric Linear Systems, Wiley-Teubner, Chichester and Stuttgart, 1996.
R. Fletcher, Practical Methods of Optimization (Second Edition), J. Wiley & Sons, Chichester, 1987.
M. Fortin and R. Glowinski, Augmented Lagrangian Methods: Application to the Solution of Boundary-Value Problems, Stud. Math. Appl., Vol. 15, North-Holland, Amsterdam, 1983.
R. W. Freund, Model reduction based on Krylov subspaces, Acta Numerica, 12 (2003), pp. 267–319.
R. W. Freund and N. M. Nachtigal, A new Krylov-subspace method for symmetric indefinite linear systems, in proceedings of 14th IMACS World Congress on Computational and Applied Mathematics, W. F. Ames, ed., IMACS, 1994, pp. 1253–1256.
R. W. Freund and N. M. Nachtigal, Software for simplified Lanczos and QMR algorithms, Appl. Numer. Math., 19 (1995), pp. 319–341.
G. H. Golub and C. Greif, On solving block-structured indefinite linear systems, SIAM J. Sci. Comput., 24 (2003), pp. 2076–2092.
N. I. M. Gould, M. E. Hribar, and J. Nocedal, On the solution of equality constrained quadratic programming problems arising in optimization, SIAM J. Sci. Comput., 23 (2001), pp. 1376–1395.
N. I. M. Gould, D. Orban, and Ph. L. Toint, CUTEr (and SifDec), a Constrained and Unconstrained Testing Environment, Revisited, Tech. Report TR/PA/01/04, CERFACS, Toulouse, France, 2001.
A. Greenbaum, Iterative Methods for Solving Linear Systems, SIAM, Philadelphia, 1997.
C. Greif and D. Schötzau, Preconditioners for the discretized time-harmonic Maxwell equations in mixed form, Numer. Linear Algebra Appl., to appear.
M. R. Hestenes and E. Stiefel, Methods of conjugate gradients for solving linear systems, J. Res. Nat. Bur. Stand., 49 (1952), pp. 409–436.
H. C. Elman, A. Ramage, and D. J. Silvester, IFISS: a Matlab toolbox for modelling incompressible flow, Manchester University Numerical Analysis Report No. 474 (2005), ACM Transactions on Mathematical Software, to appear.
C. Keller, N. I. M. Gould and A. J. Wathen, Constraint preconditioning for indefinite linear systems, SIAM J. Matrix Anal. Appl., 21 (2000), pp. 1300-1317.
C. Lanczos, An iteration method for the solution of the eigenvalue problem of linear differential and integral operators, J. Res. Nat. Bur. Stand., 45 (1950), pp. 255-282.
L. Lukšan and J. Vlček, Indefinitely preconditioned inexact Newton method for large sparse equality constrained non-linear programming problems, Numer. Linear Algebra Appl., 5 (1998), pp. 219–247.
M. F. Murphy, G. H. Golub, and A. J. Wathen, A note on preconditioning for indefinite linear systems, SIAM J. Sci. Comput., 21 (2000), pp. 1969–1972.
C. C. Paige and M. A. Saunders, Solution of sparse indefinite systems of linear equations, SIAM J. Numer. Anal., 12 (1975), pp. 617–629.
J. Nocedal and S. J. Wright, Numerical Optimization, Springer, New York, 1999.
I. Perugia and V. Simoncini, Block-diagonal and indefinite symmetric preconditioners for mixed finite element formulations, Numer. Linear Algebra Appl., 7 (2000), pp. 585–616.
T. Rusten and R. Winther, A preconditioned iterative method for saddlepoint problems, SIAM J. Matrix Anal. Appl., 13 (1992), pp. 887–904.
Y. Saad, A flexible inner-outer preconditioned GMRES, SIAM J. Sci. Comput., 14 (1993), pp. 461–469.
Y. Saad, Iterative Methods for Sparse Linear Systems, Second Edition, SIAM, Philadelphia, PA, 2003.
Y. Saad and M. H. Schultz, GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems, SIAM J. Sci. Stat. Comput., 7 (1986), pp. 856–869.
D. J. Silvester and A. J. Wathen, Fast iterative solution of stabilised Stokes systems. II: Using general block preconditioners, SIAM J. Numer. Anal., 31 (1994), pp. 1352–1367.
V. Simoncini and M. Benzi, Spectral properties of the Hermitian and skew-Hermitian splitting preconditioner for saddle point problems, SIAM J. Matrix Anal. Appl., 26 (2004), pp. 377–389.
G. L. G. Sleijpen, H. A. van der Vorst, and J. Modersitzki, Effects of rounding errors in determining approximate solutions in Krylov solvers for symmetric indefinite linear systems, SIAM J. Matrix Anal. Appl., 22 (2000), pp. 726-751.
G. Strang, Introduction to Applied Mathematics, Wellesley, Cambridge, MA, 1986.
H. A. van der Vorst, Iterative Krylov Methods for Large Linear Systems, Cambridge Monographs on Applied and Computational Mathematics, Cambridge University Press, Cambridge, UK, 2003.
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Benzi, M., Wathen, A.J. (2008). Some Preconditioning Techniques for Saddle Point Problems. In: Schilders, W.H.A., van der Vorst, H.A., Rommes, J. (eds) Model Order Reduction: Theory, Research Aspects and Applications. Mathematics in Industry, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78841-6_10
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DOI: https://doi.org/10.1007/978-3-540-78841-6_10
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