After reading this book, students should be able to analyze computational problems in linear algebra such as linear systems, least squares- and eigenvalue problems, and to develop their own algorithms for solving them. Since these problems can be large and difficult to handle, much can be gained by understanding and taking advantage of special structures. This in turn requires a good grasp of basic numerical linear algebra and matrix factorizations. Factoring a matrix into a product of simpler matrices is a crucial tool in numerical linear algebra, because it allows us to tackle complex problems by solving a sequence of easier ones. The main characteristics of this book are as follows: It is self-contained, only assuming that readers have completed first-year calculus and an introductory course on linear algebra, and that they have some experience with solving mathematical problems on a computer. The book provides detailed proofs of virtually all results. Further, its respective parts can be used independently, making it suitable for self-study. The book consists of 15 chapters, divided into five thematically oriented parts. The chapters are designed for a one-week-per-chapter, one-semester course. To facilitate self-study, an introductory chapter includes a brief review of linear algebra.

To put the world of linear algebra to advanced use, it is not enough to merely understand the theory; there is a significant gap between the theory of linear algebra and its myriad expressions in nearly every computational domain. To bridge this gap, it is essential to process the theory by solving many exercises, thus obtaining a firmer grasp of its diverse applications. Similarly, from a theoretical perspective, diving into the literature on advanced linear algebra often reveals more and more topics that are deferred to exercises instead of being treated in the main text. As exercises grow more complex and numerous, it becomes increasingly important to provide supporting material and guidelines on how to solve them, supporting students’ learning process. This book provides precisely this type of supporting material for the textbook “Numerical Linear Algebra and Matrix Factorizations,” published as Vol. 22 of Springer’s Texts in Computational Science and Engineering series. Instead of omitting details or merely providing rough outlines, this book offers detailed proofs, and connects the solutions to the corresponding results in the textbook. For the algorithmic exercises the utmost level of detail is provided in the form of MATLAB implementations. Both the textbook and solutions are self-contained. This book and the textbook are of similar length, demonstrating that solutions should not be considered a minor aspect when learning at advanced levels.

This expansive volume describes the history of numerical methods proposed for solving linear algebra problems, from antiquity to the present day. The authors focus on methods for linear systems of equations and eigenvalue problems and describe the interplay between numerical methods and the computing tools available at the time. The second part of the book consists of 78 biographies of important contributors to the field. A Journey through the History of Numerical Linear Algebra will be of special interest to applied mathematicians, especially researchers in numerical linear algebra, people involved in scientific computing, and historians of mathematics.

The purpose of Numerical Linear Algebra in Signals, Systems and Control is to present an interdisciplinary book, blending linear and numerical linear algebra with three major areas of electrical engineering: Signal and Image Processing, and Control Systems and Circuit Theory. Numerical Linear Algebra in Signals, Systems and Control will contain articles, both the state-of-the-art surveys and technical papers, on theory, computations, and applications addressing significant new developments in these areas. The goal of the volume is to provide authoritative and accessible accounts of the fast-paced developments in computational mathematics, scientific computing, and computational engineering methods, applications, and algorithms. The state-of-the-art surveys will benefit, in particular, beginning researchers, graduate students, and those contemplating to start a new direction of research in these areas. A more general goal is to foster effective communications and exchange of information between various scientific and engineering communities with mutual interests in concepts, computations, and workable, reliable practices.

Accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Regardless of the software system used, the book describes and gives examples of the use of modern computer software for numerical linear algebra. It begins with a discussion of the basics of numerical computations, and then describes the relevant properties of matrix inverses, factorisations, matrix and vector norms, and other topics in linear algebra. The book is essentially self- contained, with the topics addressed constituting the essential material for an introductory course in statistical computing. Numerous exercises allow the text to be used for a first course in statistical computing or as supplementary text for various courses that emphasise computations.

Unlock the Language of Vectors and Matrices for Enhanced Problem Solving In the realm of mathematics and science, linear algebra stands as a powerful language that underlies numerous disciplines. "Mastering Linear Algebra" is your definitive guide to understanding and harnessing the potential of this essential mathematical framework, empowering you to solve complex problems with clarity and precision. About the Book: As mathematical concepts become more integral to various fields, a strong grasp of linear algebra becomes increasingly valuable. "Mastering Linear Algebra" offers a comprehensive exploration of this foundational subject—a cornerstone of mathematics and its applications. This book caters to both newcomers and experienced learners aiming to excel in linear algebra concepts, computations, and applications. Key Features: Linear Algebra Fundamentals: Begin by understanding the core principles of linear algebra. Learn about vectors, matrices, and linear transformations—the fundamental building blocks of the subject. Matrix Operations: Dive into matrix operations. Explore techniques for matrix addition, multiplication, inversion, and determinant computation. Vector Spaces: Grasp the art of vector spaces and subspaces. Understand how to define, visualize, and analyze vector spaces for various applications. Eigenvalues and Eigenvectors: Explore the significance of eigenvalues and eigenvectors. Learn how they enable the analysis of dynamic systems and transformations. Linear Systems: Understand how linear algebra solves systems of linear equations. Explore techniques for Gaussian elimination, LU decomposition, and matrix factorization. Applications in Science and Engineering: Delve into real-world applications of linear algebra. Discover how it's applied in physics, computer graphics, data analysis, and more. Inner Product Spaces: Grasp the concepts of inner product spaces and orthogonality. Explore applications in geometric interpretations and least-squares solutions. Singular Value Decomposition: Explore the power of singular value decomposition. Understand how it enables data compression, noise reduction, and dimensionality reduction. Why This Book Matters: In a world driven by data and technological advancement, mastering linear algebra offers a competitive edge. "Mastering Linear Algebra" empowers students, researchers, scientists, and technology enthusiasts to leverage this fundamental mathematical language, enabling them to analyze and solve problems across diverse fields. Unlock the Power of Mathematical Insight: In the landscape of mathematics and science, linear algebra is the key to understanding complex relationships and transformations. "Mastering Linear Algebra" equips you with the knowledge needed to leverage linear algebra concepts, enabling you to solve intricate problems with clarity and precision. Whether you're a seasoned learner or new to the world of linear algebra, this book will guide you in building a solid foundation for effective mathematical analysis and application. Your journey to mastering linear algebra starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

"Linear Algebra: A Formula Handbook" is an indispensable reference guide that condenses the fundamental principles of linear algebra into clear, concise formulas. Covering essential topics such as vectors, matrices, systems of linear equations, eigenvalues, and eigenvectors, this handbook provides quick access to key equations and principles needed for understanding and solving linear algebra problems. Whether you're a student, researcher, or professional in mathematics, engineering, or computer science, this book serves as a valuable resource for mastering the foundational aspects of linear algebra and its applications in various fields.

Linear algebra forms the basis for much of modern mathematics—theoretical, applied, and computational. Finite-Dimensional Linear Algebra provides a solid foundation for the study of advanced mathematics and discusses applications of linear algebra to such diverse areas as combinatorics, differential equations, optimization, and approximation. The author begins with an overview of the essential themes of the book: linear equations, best approximation, and diagonalization. He then takes students through an axiomatic development of vector spaces, linear operators, eigenvalues, norms, and inner products. In addition to discussing the special properties of symmetric matrices, he covers the Jordan canonical form, an important theoretical tool, and the singular value decomposition, a powerful tool for computation. The final chapters present introductions to numerical linear algebra and analysis in vector spaces, including a brief introduction to functional analysis (infinite-dimensional linear algebra). Drawing on material from the author’s own course, this textbook gives students a strong theoretical understanding of linear algebra. It offers many illustrations of how linear algebra is used throughout mathematics.

Designed for advanced undergraduate and beginning graduate students in linear or abstract algebra, Advanced Linear Algebra covers theoretical aspects of the subject, along with examples, computations, and proofs. It explores a variety of advanced topics in linear algebra that highlight the rich interconnections of the subject to geometry, algebra, analysis, combinatorics, numerical computation, and many other areas of mathematics. The author begins with chapters introducing basic notation for vector spaces, permutations, polynomials, and other algebraic structures. The following chapters are designed to be mostly independent of each other so that readers with different interests can jump directly to the topic they want. This is an unusual organization compared to many abstract algebra textbooks, which require readers to follow the order of chapters. Each chapter consists of a mathematical vignette devoted to the development of one specific topic. Some chapters look at introductory material from a sophisticated or abstract viewpoint, while others provide elementary expositions of more theoretical concepts. Several chapters offer unusual perspectives or novel treatments of standard results. A wide array of topics is included, ranging from concrete matrix theory (basic matrix computations, determinants, normal matrices, canonical forms, matrix factorizations, and numerical algorithms) to more abstract linear algebra (modules, Hilbert spaces, dual vector spaces, bilinear forms, principal ideal domains, universal mapping properties, and multilinear algebra). The book provides a bridge from elementary computational linear algebra to more advanced, abstract aspects of linear algebra needed in many areas of pure and applied mathematics.

'The numerical algorithms presented are written in pseudocode and based on MATLAB, a programming and numeric computing platform widely used in STEM fields. Thus, no formal training in computer science or knowledge of any specific programming language is needed to parse the algorithms. Summing up: Recommended.'CHOICEMany students come to numerical linear algebra from science and engineering seeking modern tools and an understanding of how the tools work and their limitations. Often their backgrounds and experience are extensive in applications of numerical methods but limited in abstract mathematics and matrix theory. Often enough it is limited to multivariable calculus, basic differential equations and methods of applied mathematics. This book introduces modern tools of numerical linear algebra based on this background, heavy in applied analysis but light in matrix canonical forms and their algebraic properties. Each topic is presented as algorithmic ideas and through a foundation based on mostly applied analysis. By picking a path through the book appropriate for the level, it has been used for both senior level undergraduates and beginning graduate classes with students from diverse fields and backgrounds.