Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)(3rd Edition)

This third edition offers expanded treatment of areas such as CS decomposition, floating point arithmetic, special linear systems, the unsymmetric Lanczos process, and Toeplitz matrix eigenproblems. Other changes to this edition include some 300 new references and 100 new problems, replacement of LINPACK and EISPACK citations with pointers to LAPACK with key codes tabulated at the beginning of appropriate chapters, and correction of the large number of typographical errors in the previous edition. Copyright c 1999 Book News, Inc., Portland, OR All rights reserved - This text refers to an out of print or unavailable edition of this title. Revised And Updated, The Third Edition Of Golub And Van Loan's Classic Text In Computer Science Provides Essential Information About The Mathematical Background And Algorithmic Skills Required For The Production Of Numerical Software. This New Edition Includes Thoroughly Revised Chapters On Matrix Multiplication Problems And Parallel Matrix Computations, Expanded Treatment Of Cs Decomposition, An Updated Overview Of Floating Point Arithmetic, A More Accurate Rendition Of The Modified Gram-schmidt Process, And New Material Devoted To Gmres, Qmr, And Other Methods Designed To Handle The Sparse Unsymmetric Linear System Problem. Matrix Multiplication Problems -- Matrix Analysis -- General Linear Systems -- Special Linear Systems -- Orthogonalization And Least Squares -- Parallel Matrix Computations -- The Unsymmetric Eigenvalue Problem -- T The Symmetric Eigenvalue Problem -- Lanczos Methods -- Iterative Methods For Linear Systems -- Functions Of Matrices -- Special Topics. Gene H. Golub, Charles F. Van Loan. Includes Bibliographical References (p. 637-686) And Index. "Thoroughly revised, updated, and expanded by more than one third, this new edition of Golub and Van Loan's landmark book in scientific computing provides the vital mathematical background and algorithmic skills required for the production of numerical software. New chapters on high performance computing use matrix multiplication to show how to organize a calculation for vector processors as well as for computers with shared or distributed memories. A.so new are discussions of parallel vector methods for linear equations, least squares, and eigenvalue problems."--Back cover. This revised edition provides the mathematical background and algorithmic skills required for the production of numerical software. It includes rewritten and clarified proofs and derivations, as well as new topics such as Arnoldi iteration, and domain decomposition methods.