Approximate Factorization Constraint Preconditioners for Saddle-Point Matrices | Zendy

H. Sue Dollar | Zendy; Andy Wathen | Zendy

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Approximate Factorization Constraint Preconditioners for Saddle-Point Matrices

Author(s) -

H. Sue Dollar,

Andy Wathen

Publication year - 2006

Publication title -

siam journal on scientific computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.674

H-Index - 147

eISSN - 1095-7197

pISSN - 1064-8275

DOI - 10.1137/04060768x

Subject(s) - mathematics , conjugate gradient method , saddle point , eigenvalues and eigenvectors , factorization , preconditioner , conjugate residual method , polynomial , constraint (computer aided design) , linear system , interior point method , matrix (chemical analysis) , saddle , mathematical optimization , algorithm , mathematical analysis , gradient descent , computer science , geometry , physics , materials science , quantum mechanics , machine learning , artificial neural network , composite material

We consider the application of the conjugate gradient method to the solution of large, symmetric indefinite linear systems. Special emphasis is put on the use of constraint preconditioners and a new factorization that can reduce the number of flops required by the preconditioning step. Results concerning the eigenvalues of the preconditioned matrix and its minimum polynomial are given. Numerical experiments validate these conclusions. © 2006 Society for Industrial and Applied Mathematics

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