Advertisement
| linear algebra quick review: No Bullshit Guide to Linear Algebra Ivan Savov, 2020-10-25 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. |
| linear algebra quick review: How to Prove It Daniel J. Velleman, 2006-01-16 Many students have trouble the first time they take a mathematics course in which proofs play a significant role. This new edition of Velleman's successful text will prepare students to make the transition from solving problems to proving theorems by teaching them the techniques needed to read and write proofs. The book begins with the basic concepts of logic and set theory, to familiarize students with the language of mathematics and how it is interpreted. These concepts are used as the basis for a step-by-step breakdown of the most important techniques used in constructing proofs. The author shows how complex proofs are built up from these smaller steps, using detailed 'scratch work' sections to expose the machinery of proofs about the natural numbers, relations, functions, and infinite sets. To give students the opportunity to construct their own proofs, this new edition contains over 200 new exercises, selected solutions, and an introduction to Proof Designer software. No background beyond standard high school mathematics is assumed. This book will be useful to anyone interested in logic and proofs: computer scientists, philosophers, linguists, and of course mathematicians. |
| linear algebra quick review: A First Course in Linear Algebra Kenneth Kuttler, Ilijas Farah, 2020 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. |
| linear algebra quick review: Basic Linear Algebra T.S. Blyth, E.F. Robertson, 2013-12-01 Basic Linear Algebra is a text for first year students leading from concrete examples to abstract theorems, via tutorial-type exercises. More exercises (of the kind a student may expect in examination papers) are grouped at the end of each section. The book covers the most important basics of any first course on linear algebra, explaining the algebra of matrices with applications to analytic geometry, systems of linear equations, difference equations and complex numbers. Linear equations are treated via Hermite normal forms which provides a successful and concrete explanation of the notion of linear independence. Another important highlight is the connection between linear mappings and matrices leading to the change of basis theorem which opens the door to the notion of similarity. This new and revised edition features additional exercises and coverage of Cramer's rule (omitted from the first edition). However, it is the new, extra chapter on computer assistance that will be ofparticular interest to readers: this will take the form of a tutorial on the use of the LinearAlgebra package in MAPLE 7 and will deal with all the aspects of linear algebra developed within the book. |
| linear algebra quick review: Schaum's Outline of Theory and Problems of Linear Algebra Seymour Lipschutz, 1991 Basic definitions, explorations of principles and theorems, and solved problems provide a theoretical framework and computational tool for understanding linear algebra |
| linear algebra quick review: Linear Algebra and Linear Models R. B. Bapat, 2000 This book provides a rigorous introduction to the basic aspects of the theory of linear estimation and hypothesis testing, covering the necessary prerequisites in matrices, multivariate normal distribution and distributions of quadratic forms along the way. It will appeal to advanced undergraduate and first-year graduate students, research mathematicians and statisticians. |
| linear algebra quick review: Linear Algebra Done Right Sheldon Axler, 1997-07-18 This text for a second course in linear algebra, aimed at math majors and graduates, adopts a novel approach by banishing determinants to the end of the book and focusing on understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space has an eigenvalue. The book starts by discussing vector spaces, linear independence, span, basics, and dimension. Students are introduced to inner-product spaces in the first half of the book and shortly thereafter to the finite- dimensional spectral theorem. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition features new chapters on diagonal matrices, on linear functionals and adjoints, and on the spectral theorem; some sections, such as those on self-adjoint and normal operators, have been entirely rewritten; and hundreds of minor improvements have been made throughout the text. |
| linear algebra quick review: Linear Algebra for Everyone Gilbert Strang, 2020-11-26 Linear algebra has become the subject to know for people in quantitative disciplines of all kinds. No longer the exclusive domain of mathematicians and engineers, it is now used everywhere there is data and everybody who works with data needs to know more. This new book from Professor Gilbert Strang, author of the acclaimed Introduction to Linear Algebra, now in its fifth edition, makes linear algebra accessible to everybody, not just those with a strong background in mathematics. It takes a more active start, beginning by finding independent columns of small matrices, leading to the key concepts of linear combinations and rank and column space. From there it passes on to the classical topics of solving linear equations, orthogonality, linear transformations and subspaces, all clearly explained with many examples and exercises. The last major topics are eigenvalues and the important singular value decomposition, illustrated with applications to differential equations and image compression. A final optional chapter explores the ideas behind deep learning. |
| linear algebra quick review: Linear Algebra for Dummies Mary Jane Sterling, 2009 |
| linear algebra quick review: A Quick Steep Climb Up Linear Algebra Stephen Davies, 2021-01-13 A Quick Steep Climb Up Lienar Algebra - and its companion site allthemath - are completely-and-forever-free-and-open-source educational materials dedicated to the mathematics that budding computer science practitioners actually need to know. They feature the fun and addictive teaching of award-winning lecturer Dr. Stephen Davies of the University of Mary Washington in Fredericksburg, Virginia! |
| linear algebra quick review: A First Course in Linear Algebra Mohammed K A Kaabar, 2014-10-20 In this book, there are five chapters: Systems of Linear Equations, Vector Spaces, Homogeneous Systems, Characteristic Equation of Matrix, and Matrix Dot Product. It is also included exercises at the end of each chapter above to let students practice additional sets of problems other than examples, and they can also check their solutions to some of these exercises by looking at “Answers to Odd-Numbered Exercises” section at the end of this book. This book is very useful for college students who studied Calculus I, and other students who want to review some linear algebra concepts before studying a second course in linear algebra. |
| linear algebra quick review: Advanced Linear Algebra Bruce Cooperstein, 2010-06-23 Advanced Linear Algebra focuses on vector spaces and the maps between them that preserve their structure (linear transformations). It starts with familiar concepts and then slowly builds to deeper results. Along with including many exercises and examples, each section reviews what students need to know before studying the material. The book first introduces vector spaces over fields as well as the fundamental concepts of linear combinations, span of vectors, linear independence, basis, and dimension. After covering linear transformations, it discusses the algebra of polynomials with coefficients in a field, concentrating on results that are consequences of the division algorithm. The author then develops the whole structure theory of a linear operator on a finite dimensional vector space from a collection of some simple results. He also explores the entire range of topics associated with inner product spaces, from the Gram–Schmidt process to the spectral theorems for normal and self-adjoint operators on an inner product space. The text goes on to rigorously describe the trace and determinant of linear operators and square matrices. The final two chapters focus on bilinear forms and tensor products and related material. Designed for advanced undergraduate and beginning graduate students, this textbook shows students the beauty of linear algebra. It also prepares them for further study in mathematics. |
| linear algebra quick review: Quick Algebra Review Peter H. Selby, Steve Slavin, 1993-03-10 The fastest, easiest way to brush up on your algebra! Quick AlgebraReview Need to hone your algebra skills? This bestselling reviewcourse in intermediate algebra gives you all the concepts,procedures, and problem-solving methods you need to succeed. * Each chapter begins with an easy-to-use chart that zeroes in onyour problem areas. Now you can avoid wasting hours rehashingfamiliar concepts. * Every key algebraic concept is covered thoroughly--includingpositive and negative numbers, fractions, rational numbers,factoring, linear equations, quadratic equations, and wordproblems. * Hundreds of questions, answers, review problems, and quizzes helpyou to test your progress every step of the way. Now updated and revised to be even more relevant and accessiblethan ever, Quick Algebra Review is packed with practical examplesdrawn from real-life situations. Cover Design: Donald Munson |
| linear algebra quick review: Linear Algebra Jim Hefferon, 2014-03 The approach is developmental. Although it covers the requisite material by proving things, it does not assume that students are already able at abstract work. Instead, it proceeds with a great deal of motivation, many computational examples, and exercises that range from routine verifications to (a few) challenges. The goal is, in the context of developing the usual material of an undergraduate linear algebra course, to help raise each student's level of mathematical maturity. |
| linear algebra quick review: Mathematics for Machine Learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong, 2020-04-23 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. |
| linear algebra quick review: Practice Makes Perfect Linear Algebra (EBOOK) Sandra Luna McCune, William D. Clark, 2013-01-04 Expert instruction and plenty of practice to reinforce advanced math skills Presents concepts with application to natural sciences, engineering, economics, computer science, and other branches of mathematics Complementary to most linear algebra courses or as a refresher text More than 500 exercises and answers Hundreds of solved problems The Practice Makes Perfect series has sold more than 1 million copies worldwide |
| linear algebra quick review: Elementary Linear Algebra Stephen Andrilli, David Hecker, 2010-02-04 Elementary Linear Algebra develops and explains in careful detail the computational techniques and fundamental theoretical results central to a first course in linear algebra. This highly acclaimed text focuses on developing the abstract thinking essential for further mathematical study The authors give early, intensive attention to the skills necessary to make students comfortable with mathematical proofs. The text builds a gradual and smooth transition from computational results to general theory of abstract vector spaces. It also provides flexbile coverage of practical applications, exploring a comprehensive range of topics. Ancillary list:* Maple Algorithmic testing- Maple TA- www.maplesoft.com - Includes a wide variety of applications, technology tips and exercises, organized in chart format for easy reference - More than 310 numbered examples in the text at least one for each new concept or application - Exercise sets ordered by increasing difficulty, many with multiple parts for a total of more than 2135 questions - Provides an early introduction to eigenvalues/eigenvectors - A Student solutions manual, containing fully worked out solutions and instructors manual available |
| linear algebra quick review: Linear Algebra As An Introduction To Abstract Mathematics Bruno Nachtergaele, Anne Schilling, Isaiah Lankham, 2015-11-30 This is an introductory textbook designed for undergraduate mathematics majors with an emphasis on abstraction and in particular, the concept of proofs in the setting of linear algebra. Typically such a student would have taken calculus, though the only prerequisite is suitable mathematical grounding. The purpose of this book is to bridge the gap between the more conceptual and computational oriented undergraduate classes to the more abstract oriented classes. The book begins with systems of linear equations and complex numbers, then relates these to the abstract notion of linear maps on finite-dimensional vector spaces, and covers diagonalization, eigenspaces, determinants, and the Spectral Theorem. Each chapter concludes with both proof-writing and computational exercises. |
| linear algebra quick review: Applied Numerical Linear Algebra James W. Demmel, 1997-01-01 Designed for use by first-year graduate students from a variety of engineering and scientific disciplines, this comprehensive textbook covers the solution of linear systems, least squares problems, eigenvalue problems, and the singular value decomposition. The author, who helped design the widely-used LAPACK and ScaLAPACK linear algebra libraries, draws on this experience to present state-of-the-art techniques for these problems, including recommendations of which algorithms to use in a variety of practical situations. Algorithms are derived in a mathematically illuminating way, including condition numbers and error bounds. Direct and iterative algorithms, suitable for dense and sparse matrices, are discussed. Algorithm design for modern computer architectures, where moving data is often more expensive than arithmetic operations, is discussed in detail, using LAPACK as an illustration. There are many numerical examples throughout the text and in the problems at the ends of chapters, most of which are written in Matlab and are freely available on the Web. Demmel discusses several current research topics, making students aware of both the lively research taking place and connections to other parts of numerical analysis, mathematics, and computer science. Some of this material is developed in questions at the end of each chapter, which are marked Easy, Medium, or Hard according to their difficulty. Some questions are straightforward, supplying proofs of lemmas used in the text. Others are more difficult theoretical or computing problems. Questions involving significant amounts of programming are marked Programming. The computing questions mainly involve Matlab programming, and others involve retrieving, using, and perhaps modifying LAPACK code from NETLIB. |
| linear algebra quick review: The Manga Guide to Linear Algebra Shin Takahashi, Iroha Inoue, 2012 The Manga Guide to Linear Algebra uses Japanese comics, clear explanations, and a charming storyline to explain the essentials of linear algebra. |
| linear algebra quick review: Linear Algebra and Its Applications Peter D. Lax, 2013-05-20 This set features Linear Algebra and Its Applications, Second Edition (978-0-471-75156-4) Linear Algebra and Its Applications, Second Edition presents linear algebra as the theory and practice of linear spaces and linear maps with a unique focus on the analytical aspects as well as the numerous applications of the subject. In addition to thorough coverage of linear equations, matrices, vector spaces, game theory, and numerical analysis, the Second Edition features student-friendly additions that enhance the book's accessibility, including expanded topical coverage in the early chapters, additional exercises, and solutions to selected problems. Beginning chapters are devoted to the abstract structure of finite dimensional vector spaces, and subsequent chapters address convexity and the duality theorem as well as describe the basics of normed linear spaces and linear maps between normed spaces. Further updates and revisions have been included to reflect the most up-to-date coverage of the topic, including: The QR algorithm for finding the eigenvalues of a self-adjoint matrix The Householder algorithm for turning self-adjoint matrices into tridiagonal form The compactness of the unit ball as a criterion of finite dimensionality of a normed linear space Additionally, eight new appendices have been added and cover topics such as: the Fast Fourier Transform; the spectral radius theorem; the Lorentz group; the compactness criterion for finite dimensionality; the characterization of commentators; proof of Liapunov's stability criterion; the construction of the Jordan Canonical form of matrices; and Carl Pearcy's elegant proof of Halmos' conjecture about the numerical range of matrices. Clear, concise, and superbly organized, Linear Algebra and Its Applications, Second Edition serves as an excellent text for advanced undergraduate- and graduate-level courses in linear algebra. Its comprehensive treatment of the subject also makes it an ideal reference or self-study for industry professionals. and Functional Analysis (978-0-471-55604-6) both by Peter D. Lax. |
| linear algebra quick review: Linear Algebra and Learning from Data Gilbert Strang, 2019-01-31 Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation. |
| linear algebra quick review: Matrix Algebra James E. Gentle, 2007-08-06 Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. The first part of this book presents the relevant aspects of the theory of matrix algebra for applications in statistics. This part begins with the fundamental concepts of vectors and vector spaces, next covers the basic algebraic properties of matrices, then describes the analytic properties of vectors and matrices in the multivariate calculus, and finally discusses operations on matrices in solutions of linear systems and in eigenanalysis. This part is essentially self-contained. The second part of the book begins with a consideration of various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. The second part also describes some of the many applications of matrix theory in statistics, including linear models, multivariate analysis, and stochastic processes. The brief coverage in this part illustrates the matrix theory developed in the first part of the book. The first two parts of the book can be used as the text for a course in matrix algebra for statistics students, or as a supplementary text for various courses in linear models or multivariate statistics. The third part of this book covers numerical linear algebra. It begins with a discussion of the basics of numerical computations, and then describes accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Although the book is not tied to any particular software system, it describes and gives examples of the use of modern computer software for numerical linear algebra. This part is essentially self-contained, although it assumes some ability to program in Fortran or C and/or the ability to use R/S-Plus or Matlab. This part of the book can be used as the text for a course in statistical computing, or as a supplementary text for various courses that emphasize computations. The book includes a large number of exercises with some solutions provided in an appendix. |
| linear algebra quick review: Introduction to Applied Linear Algebra Stephen Boyd, Lieven Vandenberghe, 2018-06-07 A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples. |
| linear algebra quick review: Advanced Linear Algebra Steven Roman, 2007-12-31 Covers a notably broad range of topics, including some topics not generally found in linear algebra books Contains a discussion of the basics of linear algebra |
| linear algebra quick review: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| linear algebra quick review: Linear Algebra Charles W. Curtis, 1968 |
| linear algebra quick review: Linear Algebra and Its Applications Gilbert Strang, 1998-07 |
| linear algebra quick review: CliffsQuickReview Linear Algebra Steven A LeDuc, 2007-05-04 CliffsQuickReview course guides cover the essentials of your toughest classes. Get a firm grip on core concepts and key material, and approach your exams with newfound confidence. CliffsQuickReview Linear Algebra demystifies the topic with straightforward explanations of the fundamentals. This comprehensive guide begins with a close look at vector algebra (including position vectors, the cross product, and the triangle inequality) and matrix algebra (including square matrices, matrix addition, and identity matrices). Once you have those subjects nailed down, you'll be ready to take on topics such as Linear systems, including Gaussian elimination and elementary row operations Real Euclidean vector spaces, including the nullspace of a matrix, projection into a subspace, and the Rank Plus Nullity Theorem The determinant, including definitions, methods, and Cramer’s Rule Linear transformations, including basis vectors, standard matrix, kernal and range, and composition Eigenvalues and Eigenvectors, including definitions and illustrations, Eigenspaces, and diagonalization CliffsQuickReview Linear Algebra acts as a supplement to your textbook and to classroom lectures. Use this reference in any way that fits your personal style for study and review — the information is clearly arranged and offered in manageable units. Here are just a few of the features you’ll find in this guide: A review of core concepts Clear diagrams and loads of formulas Easy to understand definitions and explanations Plenty of examples and detailed solutions With titles available for all the most popular high school and college courses, CliffsQuickReview guides are a comprehensive resource that can help you get the best possible grades. |
| linear algebra quick review: Linear Algebra and Matrix Theory Jimmie Gilbert, Linda Gilbert, 2004 Master linear algebra with LINEAR ALGEBRA AND MATRIX THEORY! With coverage of the classification of bilinear forms, normal matrices, spectral decompositions, the Jordan form, and sequences and series of matrices, this mathematics text prepares you to succeed in this course and in math courses in your future. Numerous examples and exercises illustrate the theories and provide you with the practice you need to master difficult concepts. |
| linear algebra quick review: Elementary Linear Algebra Lawrence E. Spence, Arnold J. Insel, Stephen H. Friedberg, 2008 Ideal as a reference or quick review of the fundamentals of linear algebra, this book offers a matrix-oriented approach--with more emphasis on Euclidean n-space, problem solving, and applications, and less emphasis on abstract vector spaces. It features a variety of applications, boxed statements of important results, and a large number of numbered and unnumbered examples. Matrices, Vectors, and Systems of Linear Equations. Matrices and Linear Transformations. Determinants. Subspaces and Their Properties. Eigenvalues, Eigenvectors, and Diagonalization. Orthogonality. Vector Spaces. Complex Numbers. A professional reference for computer scientists, statisticians, and some engineers. |
| linear algebra quick review: Ultralearning Scott H. Young, 2019-08-06 Now a Wall Street Journal bestseller. Learn a new talent, stay relevant, reinvent yourself, and adapt to whatever the workplace throws your way. Ultralearning offers nine principles to master hard skills quickly. This is the essential guide to future-proof your career and maximize your competitive advantage through self-education. In these tumultuous times of economic and technological change, staying ahead depends on continual self-education—a lifelong mastery of fresh ideas, subjects, and skills. If you want to accomplish more and stand apart from everyone else, you need to become an ultralearner. The challenge of learning new skills is that you think you already know how best to learn, as you did as a student, so you rerun old routines and old ways of solving problems. To counter that, Ultralearning offers powerful strategies to break you out of those mental ruts and introduces new training methods to help you push through to higher levels of retention. Scott H. Young incorporates the latest research about the most effective learning methods and the stories of other ultralearners like himself—among them Benjamin Franklin, chess grandmaster Judit Polgár, and Nobel laureate physicist Richard Feynman, as well as a host of others, such as little-known modern polymath Nigel Richards, who won the French World Scrabble Championship—without knowing French. Young documents the methods he and others have used to acquire knowledge and shows that, far from being an obscure skill limited to aggressive autodidacts, ultralearning is a powerful tool anyone can use to improve their career, studies, and life. Ultralearning explores this fascinating subculture, shares a proven framework for a successful ultralearning project, and offers insights into how you can organize and exe - cute a plan to learn anything deeply and quickly, without teachers or budget-busting tuition costs. Whether the goal is to be fluent in a language (or ten languages), earn the equivalent of a college degree in a fraction of the time, or master multiple tools to build a product or business from the ground up, the principles in Ultralearning will guide you to success. |
| linear algebra quick review: Linear Algebra and Its Applications, Global Edition David C. Lay, Steven R. Lay, Judi J. McDonald, 2015-06-03 NOTE: Before purchasing, check with your instructor to ensure you select the correct ISBN. Several versions of Pearson's MyLab & Mastering products exist for each title, and registrations are not transferable. To register for and use Pearson's MyLab & Mastering products, you may also need a Course ID, which your instructor will provide. Used books, rentals, and purchases made outside of PearsonIf purchasing or renting from companies other than Pearson, the access codes for Pearson's MyLab & Mastering products may not be included, may be incorrect, or may be previously redeemed. Check with the seller before completing your purchase. Note: You are purchasing a standalone product; MyMathLab does not come packaged with this content. MyMathLab is not a self-paced technology and should only be purchased when required by an instructor. If you would like to purchase both the physical text and MyMathLab, search for: 9780134022697 / 0134022696 Linear Algebra and Its Applications plus New MyMathLab with Pearson eText -- Access Card Package, 5/e With traditional linear algebra texts, the course is relatively easy for students during the early stages as material is presented in a familiar, concrete setting. However, when abstract concepts are introduced, students often hit a wall. Instructors seem to agree that certain concepts (such as linear independence, spanning, subspace, vector space, and linear transformations) are not easily understood and require time to assimilate. These concepts are fundamental to the study of linear algebra, so students' understanding of them is vital to mastering the subject. This text makes these concepts more accessible by introducing them early in a familiar, concrete Rn setting, developing them gradually, and returning to them throughout the text so that when they are discussed in the abstract, students are readily able to understand. |
| linear algebra quick review: Numerical Linear Algebra Lloyd N. Trefethen, David Bau, 2022-06-30 Since its original appearance in 1997, Numerical Linear Algebra has been a leading textbook in its field. It is noted for its 40 lecture-sized short chapters and its clear and inviting style. It is reissued here with a new foreword by James Nagy and a new afterword by Yuji Nakatsukasa about subsequent developments. |
| linear algebra quick review: Linear Algebra Kenneth Hoffman, Ray Alden Kunze, 2015 |
| linear algebra quick review: Linear Algebra and Its Applications David C. Lay, 2012 Linear algebra is relatively easy for students during the early stages of the course, when the material is presented in a familiar, concrete setting. But when abstract concepts are introduced, students often hit a brick wall. Instructors seem to agree that certain concepts (such as linear independence, spanning, subspace, vector space, and linear transformations), are not easily understood, and require time to assimilate. Since they are fundamental to the study of linear algebra, students' understanding of these concepts is vital to their mastery of the subject. David Lay introduces these concepts early in a familiar, concrete Rn setting, develops them gradually, and returns to them again and again throughout the text so that when discussed in the abstract, these concepts are more accessible. Note: This is the standalone book, if you want the book/access card order the ISBN below. 0321399145 / 9780321399144 Linear Algebra plus MyMathLab Getting Started Kit for Linear Algebra and Its Applications Package consists of: 0321385179 / 9780321385178 Linear Algebra and Its Applications 0321431308 / 9780321431301 MyMathLab/MyStatLab -- Glue-in Access Card 0321654064 / 9780321654069 MyMathLab Inside Star Sticker |
| linear algebra quick review: A First Course In Linear Algebra (Custom Edition EBook) David Easdown, 2011 |
| linear algebra quick review: Elementary Linear Algebra Howard Anton, 2000 |
| linear algebra quick review: CliffsQuickReview Calculus Jonathan J White, Bernard V Zandy, 2010-12-29 CliffsQuickReview course guides cover the essentials of your toughest subjects. Get a firm grip on core concepts and key material, and test your newfound knowledge with review questions. Whether you're new to limits, derivatives, and integrals or just brushing up on your knowledge of the subject, CliffsQuickReview Calculus can help. This guide covers calculus topics such as limits at infinity, differential rules, and integration by parts. You'll also tackle other concepts, including Differentiation of inverse trigonometric functions Distance, velocity, and acceleration Volumes of solids with known cross sections Extreme value theorem Concavity and points of inflection CliffsQuickReview Calculus acts as a supplement to your other learning materials. Use this reference in any way that fits your personal style for study and review — you decide what works best with your needs. You can flip through the book until you find what you're looking for — it's organized to gradually build on key concepts. Here are just a few other ways you can search for topics: Use the free Pocket Guide full of essential information. Get a glimpse of what you’ll gain from a chapter by reading through the Chapter Check-In at the beginning of each chapter. Use the Chapter Checkout at the end of each chapter to gauge your grasp of the important information you need to know. Test your knowledge more completely in the CQR Review and look for additional sources of information in the CQR Resource Center. Tap the glossary to find key terms fast. With titles available for all the most popular high school and college courses, CliffsQuickReview guides are comprehensive resources that can help you get the best possible grades. |
| linear algebra quick review: Real Analysis and Foundations Steven G. Krantz, 1991-09-12 Real Analysis and Foundations is an advanced undergraduate and first-year graduate textbook that introduces students to introductory topics in real analysis (or real variables), point set topology, and the calculus of variations. This classroom-tested book features over 350 end-of-chapter exercises that clearly develop and reinforce conceptual topics. It also provides an excellent review chapter on math foundations topics, as well as accessible coverage of classical topics, such as Weirstrass Approximation Theorem, Ascoli-Arzela Theorem and Schroeder-Bernstein Theorem. Explanations and discussions of key concepts are so well done that Real Analysis and Foundations will also provide valuable information for professional aerospace and structural engineers. |
Linear – Plan and build products
Linear is a purpose-built tool for modern product development. Streamline issues, projects, and product roadmaps.
Download Linear
Download the Linear app for desktop and mobile. Available for Mac, Windows, iOS, and Android.
Pricing – Linear
Use Linear for free with your whole team. Upgrade to enable unlimited issues, enhanced security controls, and additional features.
Customer Requests – Linear
Capture feedback across any customer interaction – from sales calls to support chats – and turn it into a customer request linked to a Linear project or issue.
Linear MCP server – Changelog
May 1, 2025 · Your AI models and agents can use our official MCP server to access your Linear data in a simple and secure way. Connect to our MCP server natively as a new Claude …
About – Linear
Today, thousands of teams around the globe — from early-stage startups to public companies — use Linear to build their products. Linear helps them to focus on what they do best: Crafting …
Linear Method – Practices for building
The quality of a product is driven by both the talent of its creators and how they feel while they’re crafting it. To bring back the right focus, these are the foundational and evolving ideas Linear …
Features – Linear
With its meticulous design, breathtaking speed and purpose-built workflows, Linear unlocks your team’s full potential. It is the tool of choice for high-performance teams to build products better.
Linear Customers
Linear Projects give Mercury a source-of-truth across all their work. Read story. Watershed switched to Linear to accelerate their team. Read story. Linear keeps Retool's teams customer …
Building our way - Linear Blog
Jun 10, 2025 · Linear is unique because it actually manages all these workflows in a structured way and in a single product, bringing teams together. We see this end-to-end workflow as the …
Linear – Plan and build products
Linear is a purpose-built tool for modern product development. Streamline issues, projects, and product roadmaps.
Download Linear
Download the Linear app for desktop and mobile. Available for Mac, Windows, iOS, and Android.
Pricing – Linear
Use Linear for free with your whole team. Upgrade to enable unlimited issues, enhanced security controls, and additional features.
Customer Requests – Linear
Capture feedback across any customer interaction – from sales calls to support chats – and turn it into a customer request linked to a Linear project or issue.
Linear MCP server – Changelog
May 1, 2025 · Your AI models and agents can use our official MCP server to access your Linear data in a simple and secure way. Connect to our MCP server natively as a …
