Author : Stephen Boyd
Publisher : Cambridge University Press
Page : 477 pages
File Size : 26,28 MB
Release : 2018-06-07
Category : Business & Economics
ISBN : 1316518965
A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.
Author : James E. Gentle
Publisher : Springer Science & Business Media
Page : 536 pages
File Size : 13,76 MB
Release : 2007-07-27
Category : Computers
ISBN : 0387708723
Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. 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. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.
Author : Karim M. Abadir
Publisher : Cambridge University Press
Page : 472 pages
File Size : 48,82 MB
Release : 2005-08-22
Category : Business & Economics
ISBN : 9780521822893
Matrix Algebra is the first volume of the Econometric Exercises Series. It contains exercises relating to course material in matrix algebra that students are expected to know while enrolled in an (advanced) undergraduate or a postgraduate course in econometrics or statistics. The book contains a comprehensive collection of exercises, all with full answers. But the book is not just a collection of exercises; in fact, it is a textbook, though one that is organized in a completely different manner than the usual textbook. The volume can be used either as a self-contained course in matrix algebra or as a supplementary text.
Author : Kenneth Kuttler
Publisher :
Page : 586 pages
File Size : 14,71 MB
Release : 2020
Category : Algebras, Linear
ISBN :
"A First Course in Linear Algebra, originally by K. Kuttler, has been redesigned by the Lyryx editorial team as a first course for the general students who have an understanding of basic high school algebra and intend to be users of linear algebra methods in their profession, from business & economics to science students. All major topics of linear algebra are available in detail, as well as justifications of important results. In addition, connections to topics covered in advanced courses are introduced. The textbook is designed in a modular fashion to maximize flexibility and facilitate adaptation to a given course outline and student profile. Each chapter begins with a list of student learning outcomes, and examples and diagrams are given throughout the text to reinforce ideas and provide guidance on how to approach various problems. Suggested exercises are included at the end of each section, with selected answers at the end of the textbook."--BCcampus website.
Author : Nathaniel Johnston
Publisher : Springer Nature
Page : 482 pages
File Size : 27,64 MB
Release : 2021-05-19
Category : Mathematics
ISBN : 3030528111
This textbook emphasizes the interplay between algebra and geometry to motivate the study of linear algebra. Matrices and linear transformations are presented as two sides of the same coin, with their connection motivating inquiry throughout the book. By focusing on this interface, the author offers a conceptual appreciation of the mathematics that is at the heart of further theory and applications. Those continuing to a second course in linear algebra will appreciate the companion volume Advanced Linear and Matrix Algebra. Starting with an introduction to vectors, matrices, and linear transformations, the book focuses on building a geometric intuition of what these tools represent. Linear systems offer a powerful application of the ideas seen so far, and lead onto the introduction of subspaces, linear independence, bases, and rank. Investigation then focuses on the algebraic properties of matrices that illuminate the geometry of the linear transformations that they represent. Determinants, eigenvalues, and eigenvectors all benefit from this geometric viewpoint. Throughout, “Extra Topic” sections augment the core content with a wide range of ideas and applications, from linear programming, to power iteration and linear recurrence relations. Exercises of all levels accompany each section, including many designed to be tackled using computer software. Introduction to Linear and Matrix Algebra is ideal for an introductory proof-based linear algebra course. The engaging color presentation and frequent marginal notes showcase the author’s visual approach. Students are assumed to have completed one or two university-level mathematics courses, though calculus is not an explicit requirement. Instructors will appreciate the ample opportunities to choose topics that align with the needs of each classroom, and the online homework sets that are available through WeBWorK.
Author : Ivan Savov
Publisher :
Page : 596 pages
File Size : 34,29 MB
Release : 2020-10-25
Category : MATHEMATICS
ISBN : 9780992001025
This textbook covers the material for an undergraduate linear algebra course: vectors, matrices, linear transformations, computational techniques, geometric constructions, and theoretical foundations. The explanations are given in an informal conversational tone. The book also contains 100+ problems and exercises with answers and solutions. A special feature of this textbook is the prerequisites chapter that covers topics from high school math, which are necessary for learning linear algebra. The presence of this chapter makes the book suitable for beginners and the general audience-readers need not be math experts to read this book. Another unique aspect of the book are the applications chapters (Ch 7, 8, and 9) that discuss applications of linear algebra to engineering, computer science, economics, chemistry, machine learning, and even quantum mechanics.
Author : Philip J. Davis
Publisher : John Wiley & Sons
Page : 376 pages
File Size : 46,22 MB
Release : 1973
Category : Mathematics
ISBN :
Author : Mike X. Cohen
Publisher :
Page : 584 pages
File Size : 11,28 MB
Release : 2021-02
Category : Mathematics
ISBN : 9789083136608
Linear algebra is perhaps the most important branch of mathematics for computational sciences, including machine learning, AI, data science, statistics, simulations, computer graphics, multivariate analyses, matrix decompositions, signal processing, and so on.The way linear algebra is presented in traditional textbooks is different from how professionals use linear algebra in computers to solve real-world applications in machine learning, data science, statistics, and signal processing. For example, the "determinant" of a matrix is important for linear algebra theory, but should you actually use the determinant in practical applications? The answer may surprise you!If you are interested in learning the mathematical concepts linear algebra and matrix analysis, but also want to apply those concepts to data analyses on computers (e.g., statistics or signal processing), then this book is for you. You'll see all the math concepts implemented in MATLAB and in Python.Unique aspects of this book: - Clear and comprehensible explanations of concepts and theories in linear algebra. - Several distinct explanations of the same ideas, which is a proven technique for learning. - Visualization using graphs, which strengthens the geometric intuition of linear algebra. - Implementations in MATLAB and Python. Com'on, in the real world, you never solve math problems by hand! You need to know how to implement math in software! - Beginner to intermediate topics, including vectors, matrix multiplications, least-squares projections, eigendecomposition, and singular-value decomposition. - Strong focus on modern applications-oriented aspects of linear algebra and matrix analysis. - Intuitive visual explanations of diagonalization, eigenvalues and eigenvectors, and singular value decomposition. - Codes (MATLAB and Python) are provided to help you understand and apply linear algebra concepts on computers. - A combination of hand-solved exercises and more advanced code challenges. Math is not a spectator sport!
Author : SUDDHENDU BISWAS
Publisher : PHI Learning Pvt. Ltd.
Page : 454 pages
File Size : 49,89 MB
Release : 2012-08-07
Category : Mathematics
ISBN : 8120346238
Intended as a text for postgraduate and undergraduate honours students of Statistics, Mathematics, Operations Research as well as students in various branches of Engineering, this student-friendly book gives an indepth analysis of Matrix Algebra and all the major topics related to it. Divided into 12 chapters, the book begins with a discussion on Elements of Matrix Theory and Some Special Matrices. Then it goes on to give a detailed discussion on Scalar Function and Inverse of a Matrix, Rank of a Matrix, Generalized Inverse of a Matrix, and Quadric Forms and Inequalities. The book concludes by giving Some Applications of Algebra of Matrices, Matrices in the Infinite Dimensional Vector Space, and Computational Tracts in Matrices. KEY FEATURES Gives a large number of both solved and unsolved problems of Elementary Matrix. Provides an exhaustive treatment of Generalized Inverse Matrix with many applications in Statistics. Devotes one chapter exclusively to application of Matrices. Provides one full chapter on Matrices in the Infinite Dimensional Vector Space, which will be quite useful for postgraduate students. Gives an Appendix on R Software which will be extremely useful for students of Statistics. Provides Question Bank which will greatly benefit both undergraduate and postgraduate students. This book, which beautifully blends both theory and applications of Matrix Algebra, should prove to be an invaluable text for the students.
Author : Autar Kaw
Publisher : Lulu.com
Page : 185 pages
File Size : 33,84 MB
Release : 2008-09
Category : Education
ISBN : 0615251269
Since 2002, the Introduction to Matrix Algebra book has been downloaded by more than 30,000 users from 50 different countries. This book is an extended primer for undergraduate Matrix Algebra. The book is either to be used as a refresher material for students who have already taken a course in Matrix Algebra or used as a just-in-time tool if the burden of teaching Matrix Algebra has been placed on several courses. In my own department, the Linear Algebra course was taken out of the curriculum a decade ago. It is now taught just in time in courses like Statics, Programming Concepts, Vibrations, and Controls. There are ten chapters in the book 1) INTRODUCTION, 2) VECTORS, 3) BINARY MATRIX OPERATIONS, 4) UNARY MATRIX OPERATIONS, 5) SYSTEM OF EQUATIONS, 6) GAUSSIAN ELIMINATION, 7) LU DECOMPOSITION, 8) GAUSS-SEIDAL METHOD, 9) ADEQUACY OF SOLUTIONS, 10) EIGENVALUES AND EIGENVECTORS.
