Matrix Computations (Johns Hopkins Studies in the Mathematical Sciences Book 3) by Gene H Golub (PDF)

Description

The fourth edition of Gene H. Golub and Charles F. Van Loan’s classic is an essential reference for computational scientists and engineers in addition to researchers in the numerical linear algebra community. Anyone whose work requires the solution to a matrix problem and an appreciation of its mathematical properties will find this text useful and engaging. This revision is a cover-to-cover expansion and renovation of the third edition. It now includes an introduction to tensor computations and brand new sections on • fast transforms• parallel LU• discrete Poisson solvers• pseudospectra• structured linear equation problems• structured eigenvalue problems• large-scale SVD methods• polynomial eigenvalue problems Matrix Computations is packed with challenging problems, insightful derivations, and pointers to the literature—everything needed to become a matrix-savvy developer of numerical methods and software.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐The best book for matrix computation!

⭐The book has errors. I do not mean typos, but errors in programs and algorithms. I used two previous editions and order the newest hoping that it would be more error-free. What is disappointing, this book is a great source of reference and some codes and methods are really hard to find anywhere else, and are never explained as systematically. The style is not the easiest to digest, but once you passed enough numerical analysis classes, you mostly need a reference, and how attractive is this: everything in one book? I appreciate switching from fortran to matlab.

⭐cover stained with black grease, hard cover was dentedtext itself is a classic in linear algebra

⭐Golub/Van Loan is not written in a very compelling style (very formal), but it is a great reference to understand how and why numerical matrix computation work. I’ve used it extensively for developing my own Singular Value Decomposition (SVD) routine, as well as routines for general eigenvalue problems. It’s not great to use as a textbook for novices, but it is essential for use as a reference and as a teaching tool when doing anything related to numerical matrix computations. A good companion to this book is Trefethen/Bau ‘Numerical Linear Algebra’.

⭐The book content is examplary from every aspect. Highly recommend.

⭐One occasionly comes across a mathematics textbook which is so lacking in explanatory value that one is forced to wonder why it was written. This is one of those books. A sterile encyclopedia of recipes with no real attempt made to explain the ideas. It manages to make an already tedious and uninteresting subject, substatially less applealing.

⭐Amazing book. You can find absolutely anything you want in it.But heads-up: it’s not a good book if you don’t know anything about what you are going to read. It should be a “support”, but not the first book you read in this field.

⭐This is an amazing resource for understanding the numerical problems associated with implementations of matrix algorithms. I use it regularly for writing source code for solving problems that require the concepts discussed in the book.

⭐Nice

⭐Die Bewertung bezieht sich rein auf das gelieferte Produkt, der Inhalt konnte noch nicht beurteilt werden. Der Bucheinband hat leider hässliche braune Flecken, schwarze Streifen und im Licht zeichnensich Klebereste ab. Das Buch sieht eher aus als hätte es mehrere Jahre in einer Bibliothek gestanden, so dass der Zustand schwer als neu beschrieben werden kann. Mit der günstigeren gebrauchten Variante wäre ich hier wohl besser beraten gewesen. Immerhin das Seitenbild ist beim Aufschlagen tadellos.

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⭐Ein Klassiker, jetzt auch als e-book. Ich habe das Buch, aber als ich sah, dass es den Golub/VanLoan auch als e-book gibt, musste ich es unbedingt haben. Das Formel-Layout in der e-book Version ist sehr gut, keine Selbstverständlichkeit für Mathematik-lastige Texte, die als e-book mit ihrem Formel-Layout oft sehr verhunzt sind. Für jemand, der aktiv Algorithmen aus der linearen Algebra in Software-Entwicklungen einsetzt, ist dieses Buch ein absolutes Muss. Ich habe den Conjugate Gradient Algorithmus aus dem Buch mit minimalen Modifikationen in einem Graphikkarten-Programm (CUDA) erfolgreich für Bildverarbeitung eingesetzt.

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⭐La lecture de ce livre est un vrai plaisir !Une référence réactualisée avec de très nombreux algorithmes détaillés, variéset originaux, notamment de multiples variantes des factorisations QR et LU,la SVD, l’étude des erreurs d’arrondi à l’ordinateur, l’exposé toujours très clair. Des concepts fondamentaux utilisés dans des domaines aussi divers que l’informatique, l’imagerie, la météorologie, les moteurs de recherche, etc…Par rapport à la troisième édition, une centaine de pages en plus (746 pages SANS bibliographie contre 680 env. dans la 3eme INCLUANT 50 pages de bibliographie), une mise en page plus agréable, pages plus larges, et police agréable.Concernant la bibliographie de la 3eme édition, qui n’est en fait que le regroupement de toutes les références évoquées à chaque fin de section, elle a été ici retirée mais les références en fin de chaque section ont été ici conservées et de nouvelles ajoutées, par ex. dans le chapitre sur les tenseurs.Bref ce livre vaut largement son prix, un cadeau pour tous ceux qui souhaitent vivre le calcul matriciel !

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⭐Excellent book. Those who are having doubt whether to go for this or Horn and Johnson’s book, both are great books. This book’s paper quality is better than Horn’s book.

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