H.Golub Gene  | Johns Hopkins University Press

0원  | 20130101  | 9781421407944

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 book to be an indispensible tool. This revision is a cover-to-cover expansion and renovation of the third edition.

Golub, Gene H./ Van Loan, Charles F.  | Johns Hopkins University Press

0원  | 20080108  | 9780801854149

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.

 | Arcler Press

0원  | 20191101  | 9781774071441

Deals with the concept of matrix computations, a technique of singular value homogenization and its application in medical therapy. The book presents modern iterative methods to generalize the issues associated with singular-value homogenization, and explores matrix computations and the preconditioning technique of singular value homogenization.

 | Soc for Industrial & Applied Math

115,750원  | 19880101  | 9780898712278

Provides the user with a step by step introduction to Fortran 77, BLAS, LINPACK, and MATLAB. It is a reference that spans several levels of practical matrix computations with a strong emphasis on examples and "hands on" experience.

 | Springer Nature B.V.

73,480원  | 20150730  | 9789401771894

This book is primarily intended as a research monograph that could also be used in graduate courses for the design of parallel algorithms in matrix computations.It assumes general but not extensive knowledge of numerical linear algebra, parallel architectures, and parallel programming paradigms. The book consists of four parts: (I) Basics; (II) Dense and Special Matrix Computations; (III) Sparse Matrix Computations; and (IV) Matrix functions and characteristics.

Watkins, David S.  | Wiley

0원  | 20210101  | 9780471213949

Matrix computations lie at the heart of most scientific computational tasks. This volume explains computations and the accompanying theory clearly providing insight into this complex area.

 | Soc for Industrial & Applied Math

76,050원  | 19900101  | 9780898712605

Describes a selection of important parallel algorithms for matrix computations. Reviews the current status and provides an overall perspective of parallel algorithms for solving problems arising in the major areas of numerical linear algebra. The ...

Vandebril, Raf  | Johns Hopkins

135,600원  | 20210101  | 9780801890529

Many of the routines featured are coded in Matlab and can be downloaded from the Web for further exploration.

Benzi, Michele, Bini, Dario, Kressner, Daniel  | Springer

156,160원  | 20170202  | 9783319498867

Focusing on special matrices and matrices which are in some sense `near' to structured matrices, this volume covers a broad range of topics of current interest in numerical linear algebra.

Zhong-Zhi Bai, Jian-Yu Pan  | Society for Industrial & Appli

172,500원  | 20211130  | 9781611976625

Introduces the basics of matrix analysis and presents representative methods and their corresponding theories in matrix computations.

 | Scholars' Press

194,770원  | 20130830  | 9783639514919

This book considers structure preserving matrix methods for computations on Bernstein polynomials whose coefficients are corrupted by noise. The ill-posed operations of greatest common divisor computations and polynomial division are considered, and it is shown that structure preserving matrix methods yield excellent results. With respect to greatest common divisor computations, the most difficult part is the computation of its degree, and several methods for its determination are presented.

Gentle, James E.  | Springer

198,430원  | 20070713  | 9780387708720

It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices.