Skip to main content
×
×
Home
Iterative Krylov Methods for Large Linear Systems
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 224
  • Cited by
    This (lowercase (translateProductType product.productType)) has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Fisher, M. Gratton, S. Gürol, S. Trémolet, Y. and Vasseur, X. 2018. Low rank updates in preconditioning the saddle point systems arising from data assimilation problems. Optimization Methods and Software, Vol. 33, Issue. 1, p. 45.

    San, Omer and Vedula, Prakash 2018. Generalized Deconvolution Procedure for Structural Modeling of Turbulence. Journal of Scientific Computing, Vol. 75, Issue. 2, p. 1187.

    Hached, M. and Jbilou, K. 2018. Computational Krylov-based methods for large-scale differential Sylvester matrix problems. Numerical Linear Algebra with Applications, p. e2187.

    Bertaccini, Daniele and Durastante, Fabio 2018. Limited Memory Block Preconditioners for Fast Solution of Fractional Partial Differential Equations. Journal of Scientific Computing,

    Imakura, Akira Sogabe, Tomohiro and Zhang, Shao-Liang 2018. A Look-Back-type restart for the restarted Krylov subspace methods for solving non-Hermitian linear systems. Japan Journal of Industrial and Applied Mathematics,

    Pearson, John W. Pestana, Jennifer and Silvester, David J. 2018. Refined saddle-point preconditioners for discretized Stokes problems. Numerische Mathematik, Vol. 138, Issue. 2, p. 331.

    He, B. Lu, C. and Zhou, P. 2018. A Hybrid Parallel Method for 3-D Nonlinear Periodic Eddy Current Problems With Motions. IEEE Transactions on Magnetics, Vol. 54, Issue. 3, p. 1.

    Ke, Yifen Ma, Changfeng and Ren, Zhiru 2018. A new alternating positive semidefinite splitting preconditioner for saddle point problems from time-harmonic eddy current models. Frontiers of Mathematics in China, Vol. 13, Issue. 2, p. 313.

    Kalchev, Delyan Z. Manteuffel, Thomas A. and Münzenmaier, Steffen 2018. Mixed (LL∗)−1 and LL∗ least-squares finite element methods with application to linear hyperbolic problems. Numerical Linear Algebra with Applications, Vol. 25, Issue. 3, p. e2150.

    Ke, Yi-Fen and Ma, Chang-Feng 2018. A low-order block preconditioner for saddle point linear systems. Computational and Applied Mathematics, Vol. 37, Issue. 2, p. 1959.

    Kotteda, V. M. Krushnarao Kumar, Vinod and Spotz, William 2018. Performance of preconditioned iterative solvers in MFiX–Trilinos for fluidized beds. The Journal of Supercomputing,

    Ma, Junjie and Ping, Li 2017. Orthogonal AMP. IEEE Access, Vol. 5, Issue. , p. 2020.

    van der Vorst, Henk A. and Vuik, Cornelis 2017. Encyclopedia of Computational Mechanics Second Edition. p. 1.

    Bujurke, N. M. Kantli, M. H. and Shettar, Bharati M. 2017. Jacobian free Newton-GMRES method for the solution of elastohydrodynamic grease lubrication in line contact using wavelet based pre-conditioners. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences,

    He, Bo Lu, Chuan Chen, Ningning Lin, Dingsheng Rosu, Marius and Zhou, Ping 2017. TIME DECOMPOSITION METHOD FOR THE GENERAL TRANSIENT SIMULATION OF LOW-FREQUENCY ELECTROMAGNETICS. Progress In Electromagnetics Research, Vol. 160, Issue. , p. 1.

    Dehghan, Mehdi and Mohammadi-Arani, Reza 2017. Generalized product-type methods based on bi-conjugate gradient (GPBiCG) for solving shifted linear systems. Computational and Applied Mathematics, Vol. 36, Issue. 4, p. 1591.

    Zelyak, O Fallone, B G and St-Aubin, J 2017. Stability analysis of a deterministic dose calculation for MRI-guided radiotherapy. Physics in Medicine & Biology, Vol. 63, Issue. 1, p. 015011.

    Khamisov, Oleg O. and Stennikov, Valery A. 2017. Learning and Intelligent Optimization. Vol. 10556, Issue. , p. 139.

    Hached, M. and Jbilou, K. 2017. Numerical solutions to large-scale differential Lyapunov matrix equations. Numerical Algorithms,

    Kashefi, Ali and Staples, Anne E. 2017. A finite-element coarse-grid projection method for incompressible flow simulations. Advances in Computational Mathematics,

    ×
  • Export citation
  • Recommend to librarian
  • Recommend this book

    Email your librarian or administrator to recommend adding this book to your organisation's collection.

    Iterative Krylov Methods for Large Linear Systems
    • Online ISBN: 9780511615115
    • Book DOI: https://doi.org/10.1017/CBO9780511615115
    Please enter your name
    Please enter a valid email address
    Who would you like to send this to *
    ×
  • Buy the print book

Book description

Computational simulation of scientific phenomena and engineering problems often depends on solving linear systems with a large number of unknowns. This book gives insight into the construction of iterative methods for the solution of such systems and helps the reader to select the best solver for a given class of problems. The emphasis is on the main ideas and how they have led to efficient solvers such as CG, GMRES, and BI-CGSTAB. The author also explains the main concepts behind the construction of preconditioners. The reader is encouraged to gain experience by analysing numerous examples that illustrate how best to exploit the methods. The book also hints at many open problems and as such it will appeal to established researchers. There are many exercises that motivate the material and help students to understand the essential steps in the analysis and construction of algorithms.

Reviews

'Henk van der Vorst is one of the mathematicians who shaped this new area from its beginning until present and he has now published the present book in CUP's series Cambridge Monographs on Applied and Computational Mathematics. … the book will be particularly helpful in introductory university courses on numerical linear algebra. It strikes a neat balance between mathematical rigour and hands-on approaches for practical use and is therefore very well suited for courses with a mixed audience of mathematicians, engineers, and physicists. However, even the practitioner will find many tips and tricks and the more mathematically inclined can use this readable book with its 226 bibliographical items as a starting point to dive deeper into more specialized literature.'

Source: Zeitschrift für Angewandte Mathematik und Physik

‘Anyone interested in numerical analysis and in applied mathematics should read this book. It is absolutely splendid.’

Source: Numerical Algorithms

‘The book is useful and a source of valuable information …’

Source: Zentralblatt für Mathematik

‘This is a beautiful book … Reading and reviewing this book has been a most pleasant experience. I strongly recommend this text to colleagues and students.’

Source: Zeitschrift für Angewandte Mathematik und Mechanik

'… a compact but comprehensive introduction to iterative methods, also taking account of computer methods.'

Source: Mathematika

Refine List
Actions for selected content:
Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Send to Kindle
  • Send to Dropbox
  • Send to Google Drive
  • Send content to

    To send content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to .

    To send content items to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.

    Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

    Find out more about the Kindle Personal Document Service.

    Please be advised that item(s) you selected are not available.
    You are about to send
    ×

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
×

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 628 *
Loading metrics...

Book summary page views

Total views: 770 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 13th June 2018. This data will be updated every 24 hours.