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Linear Algebra, Matrix Algebra, and Linear Systems
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• Linear Algebra Done Right (Sheldon Axler)

The text focuses on the central goal of linear algebra: understanding the structure of linear operators on finite-dimensional vector spaces. The author has taken unusual care to motivate concepts and simplify proofs.

• 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.

• Linear Algebra: Theory, Intuition, Code (Mike X Cohen)

Are you ready to dive into the vibrant world of linear algebra and see how it powers real-world applications? Welcome to this comprehensive guide, where traditional theory meets modern computational practices.

• Hilbert Spaces And Operators On Hilbert Spaces (Leif Mejlbro)

The book concisely presents the fundamental aspects of the theory of operators on Hilbert Spaces. The topics covered include functional calculus and spectral theorems, compact operators, trace class and Hilbert-Schmidt operators, etc.

• Exercises and Problems in Linear Algebra (John M Erdman)

This book contains an extensive collection of exercises and problems that address relevant topics in linear algebra. The exercises will be both interesting and helpful to an average student.

• Linear Algebra with Python (Sean Fitzpatrick)

This textbook is for those who want to learn linear algebra from the basics. Python is used throughout the book to explain linear algebra. Learning with Python interactively, readers will naturally become accustomed to Python coding.

• Understanding Linear Algebra (David Austin)

The text aims to support readers as they develop their ability to think about linear algebra conceptually, their computational fluency (with SageMath), and their understanding of the role that linear algebra plays in shaping our society.

• Advanced Linear Algebra (David Surowski)

This book 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.

• 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.

• Linear Algebra (David Cherney, et al.)

This book moves directly to the classical topics of linear equations, fundamental subspaces, least squares, eigenvalues and singular values – in each case expressing the key idea as a matrix factorization.

• 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.

• Notes for Computational Linear Algebra (Jessy Grizzle, et al.)

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.

• 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.

• A First Course in Linear Algebra (Robert A Beezer)

Linear Algebra: Foundations to Frontiers (LAFF) is packed full of challenging, rewarding material that is essential for mathematicians, engineers, scientists, and anyone working with large datasets.

• Linear Algebra with Applications (W. Keith Nicholson)

The aim of the text is to achieve a balance among computational skills, theory, and applications of linear algebra. It is a relatively advanced introduction to the ideas and techniques of linear algebra targeted for science and engineering students.

• Applied Linear Algebra: Vectors, Matrices, and Least Squares

Combines straightforward explanations with a wealth of practical examples to offer an innovative approach to teaching linear algebra. Requiring no prior knowledge of the subject, it covers the aspects of linear algebra - vectors, matrices, and least squares

• Linear Algebra: A Course for Physicists and Engineers (Arak Mathai)

This textbook on linear algebra is written to be easy to digest by non-mathematicians. It introduces the concepts of vector spaces and mappings between them without too much theorems and proofs. Various applications of the formal theory are discussed as well.

• Introduction to Linear Algebra for Science and Engineering

This book helps Science and Engineering students understand and apply theory and concepts by combining theory with computations and slowly bringing students to the difficult abstract concepts.

• Applied Linear Algebra in Action (Vasilios N. Katsikis)

This textbook contains a collection of six high-quality chapters. The techniques are illustrated by a wide sample of applications. This book is devoted to Linear Mathematics by presenting problems in Applied Linear Algebra of general or special interest.

• Linear Algebra as an Introduction to Abstract Mathematics

This book aims to bridge the gap between the mainly computation-oriented lower division undergraduate classes and the abstract mathematics encountered in more advanced mathematics courses.

• Linear Algebra - Theorems and Applications (Hassan Abid Yasser)

This book contains selected topics in linear algebra, which represent the recent contributions in the most famous and widely problems. It includes a wide range of theorems and applications in different branches of linear algebra, etc.

• Algorithms for Sparse Linear Systems (Jennifer Scott, et al.)

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.

• 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.

• Analysis and Linear Algebra for Finance (Patrick Roger)

This book presents the elements of analysis and linear algebra used in financial models and in microeconomics. Functions of one and several variables and matrices are developed as well as vector spaces, linear mappings and optimization methods, etc.

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