Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Algorithms for Sparse Linear Systems
Top Free Machine Learning Books 🌠 - 100% Free or Open Source!
  • Title: Algorithms for Sparse Linear Systems
  • Author(s) Jennifer Scott, Miroslav Tůma
  • Publisher: Birkhäuser; 1st ed. (April 30, 2023); eBook (Creative Commons Licensed)
  • License(s): Creative Commons License (CC)
  • Hardcover: 264 pages
  • eBook: PDF and ePub
  • Language: English
  • ISBN-10: 3031258193
  • ISBN-13: 978-3031258190
  • Share This:  

Book Description

Large sparse linear systems of equations are ubiquitous in science, engineering and beyond. This open access monograph focuses on factorization algorithms for solving such systems.

About the Authors
  • Jennifer Scott is a Professor of Mathematics at the University of Reading and an Individual Merit Research Fellow at the Rutherford Appleton Laboratory.
Reviews and Rating: Related Book Categories: Read and Download Links: Similar Books:
  • Iterative Methods for Sparse Linear Systems (Yousef Saad)

    This book is a practical algorithms for solving large-scale linear systems of equations using Iterative Methods. Numerous exercises have been added, as well as an updated and expanded bibliography.

  • Templates for the Solution of Linear Systems: Building Blocks

    In this book, which focuses on the use of iterative methods for solving large sparse systems of linear equations, templates are introduced to meet the needs of both the traditional user and the high-performance specialist.

  • Numerical Methods for Large Eigenvalue Problems (Yousef Saad)

    This book is intended for researchers in applied mathematics and scientific computing as well as for practitioners interested in understanding the theory of numerical methods used for eigenvalue problems.

  • Linear Algebra (Jim Hefferon)

    This textbook covers linear systems and Gauss' method, vector spaces, linear maps and matrices, determinants, and eigenvectors and eigenvalues. Each chapter has three or four discussions of additional topics and applications.

  • A First Course in Linear Algebra (Ken Kuttler)

    The book presents an introduction to the fascinating subject of linear algebra. As the title suggests, this text is designed as a first course in linear algebra for students who have a reasonable understanding of basic algebra.

  • Fundamentals of Matrix Algebra (Gregory Hartman)

    A college (or advanced high school) level text dealing with the basic principles of matrix and linear algebra. It covers solving systems of linear equations, matrix arithmetic, the determinant, eigenvalues, and linear transformations.

  • Matrix Algebra (Marco Taboga)

    This is a course in matrix algebra, with a focus on concepts that are often used in probability and statistics. Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory.

  • Matrix Algebra with Computational Applications (Dirk Colbry)

    This book is designed to introduce students to the use of Linear Algebra to solve real-world problems. These materials were developed specifically for students and instructors that emphasizes hands-on problem-solving activities.

  • Lecture Notes of Matrix Computations (Wen-Wei Lin)

    Thoroughly details matrix computations and the accompanying theory alongside the author's useful insights, This book provides a clear and thorough introduction to matrix computations,a key component of scientific computing.

  • Linear Algebra, Theory And Applications (Kenneth Kuttler)

    This is a book on linear algebra and matrix theory. It gives a self- contained treatment of linear algebra with many of its most important applications which does not neglect arbitrary fields of scalars and the proofs of the theorems.

  • Applied and Computational Linear Algebra: A First Course

    This book is intended as a text for a graduate course that focuses on applications of linear algebra and on the algorithms used to solve the problems that arise in those applications. It uses matrix theory to understand the workings of the algorithms.

  • Advanced Linear Algebra: Foundations to Frontiers (Robert Geijn)

    The focus is on numerical linear algebra, the study of how theory, algorithms, and computer arithmetic interact. These materials keep the learner engaged by intertwining text, videos, exercises, and programming activities in consumable chunks.

  • Linear Algebra: Foundations to Frontiers (M. Myers, et al.)

    This book is an introduction to the basic concepts of linear algebra, along with an introduction to the techniques of formal mathematics. It begins with systems of equations and matrix algebra before moving into the advanced topics.

  • Computational Linear Algebra and N-dimensional Geometry

    This undergraduate textbook on Linear Algebra and n-Dimensional Geometry, in a self-teaching style, is invaluable for sophomore level undergraduates in mathematics, engineering, business, and the sciences.

  • Computational Methods of Linear Algebra (V. N. Faddeeva)

    This book presents methods for the computational solution of some important problems of linear algebra: linear systems, linear least squares problems, eigenvalue problems, and linear programming problems.

Book Categories
:
Other Categories
Resources and Links