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 Bronson, Richard, author.
 Fourth edition / Richard Bronson, Gabriel B. Costa, John T. Saccoman, Daniel Gross.  Amsterdam : Academic Press, 2023.
 Description
 Book — 1 online resource
 Summary

Linear Algebra: Algorithms, Applications, and Techniques, Fourth Edition offers a modern and algorithmic approach to computation while providing clear and straightforward theoretical background information. The book guides readers through the major applications, with chapters on properties of real numbers, proof techniques, matrices, vector spaces, linear transformations, eigen values, and Euclidean inner products. Appendices on Jordan canonical forms and Markov chains are included for further study. This useful textbook presents broad and balanced views of theory, with key material highlighted and summarized in each chapter. To further support student practice, the book also includes ample exercises with answers and hints.
2. Linear algebra [2021]
 O'Leary, Michael L., author.
 Hoboken, NJ : Wiley, 2021.
 Description
 Book — 1 online resource
 Summary

 Preface xi Acknowledgments xv
 1 Logic and Set Theory 1 1.1 Statements 1 Connectives 2 Logical Equivalence 3 1.2 Sets and Quantification 7 Universal Quantification 8 Existential Quantification 9 Negating Quantification 10 SetBuilder Notation 12 Set Operations 13 Families of Sets 14 1.3 Sets and Proofs 18 Direct Proof 20 Subsets 22 Set Equality 23 Indirect Proof 24 Mathematical Induction 25 1.4 Functions 30 Injections 33 Surjections 35 Bijections and Inverses 37 Images and Inverse Images 40 Operations 41
 2 Euclidean Space 49 2.1 Vectors 49 Vector Operations 51 Distance and Length 57 Lines and Planes 64 2.2 Dot Product 74 Lines and Planes 77 Orthogonal Projection 82 2.3 Cross Product 88 Properties 91 Areas and Volumes 93
 3 Transformations and Matrices 99 3.1 Linear Transformations 99 Properties 103 Matrices 106 3.2 Matrix Algebra 116 Addition, Subtraction, and Scalar Multiplication 116 Properties 119 Multiplication 122 Identity Matrix 129 Distributive Law 132 Matrices and Polynomials 132 3.3 Linear Operators 137 Re_ections 137 Rotations 142 Isometries 147 Contractions, Dilations, and Shears 150 3.4 Injections and Surjections 155 Kernel 155 Range 158 3.5 GaussJordan Elimination 162 Elementary Row Operations 164 Square Matrices 167 Nonsquare Matrices 171 Gaussian Elimination 177
 4 Invertibility 183 4.1 Invertible Matrices 183 Elementary Matrices 186 Finding the Inverse of a Matrix 192 Systems of Linear Equations 194 4.2 Determinants 198 Multiplying a Row by a Scalar 203 Adding a Multiple of a Row to Another Row 205 Switching Rows 210 4.3 Inverses and Determinants 215 Uniqueness of the Determinant 216 Equivalents to Invertibility 220 Products 222 4.4 Applications 227 The Classical Adjoint 228 Symmetric and Orthogonal Matrices 229 Cramer's Rule 234 LU Factorization 236 Area and Volume 238
 5 Abstract Vectors 245 5.1 Vector Spaces 245 Examples of Vector Spaces 247 Linear Transformations 253 5.2 Subspaces 259 Examples of Subspaces 260 Properties 261 Spanning Sets 264 Kernel and Range 266 5.3 Linear Independence 272 Euclidean Examples 274 Abstract Vector Space Examples 276 5.4 Basis and Dimension 281 Basis 281 Zorn's Lemma 285 Dimension 287 Expansions and Reductions 290 5.5 Rank and Nullity 296 RankNullity Theorem 297 Fundamental Subspaces 302 Rank and Nullity of a Matrix 304 5.6 Isomorphism 310 Coordinates 315 Change of Basis 320 Matrix of a Linear Transformation 324
 6 Inner Product Spaces 335 6.1 Inner Products 335 Norms 341 Metrics 342 Angles 344 Orthogonal Projection 347 6.2 Orthonormal Bases 352 Orthogonal Complement 355 Direct Sum 357 GramSchmidt Process 361 QR Factorization 366
 7 Matrix Theory 373 7.1 Eigenvectors and Eigenvalues 373 Eigenspaces 375 Characteristic Polynomial 377 CayleyHamilton Theorem 382 7.2 Minimal Polynomial 386 Invariant Subspaces 389 Generalized Eigenvectors 391 Primary Decomposition Theorem 393 7.3 Similar Matrices 402 Schur's Lemma 405 Block Diagonal Form 408 Nilpotent Matrices 412 Jordan Canonical Form 415 7.4 Diagonalization 422 Orthogonal Diagonalization 426 Simultaneous Diagonalization 428 Quadratic Forms 432 Further Reading 441 Index 443.
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3. Hot topics in linear algebra [2020]
 New York : Nova Science Publishers, Inc., [2020]
 Description
 Book — 1 online resource.
 Summary

 Preface
 Computing Generalized Inverses Using GradientBased Dynamical Systems
 Cramers Rules for SylvesterType Matrix Equations
 BICR Algorithm for Computing Generalized Bisymmetric Solutions of General Coupled Matrix Equations
 System of Mixed Generalized SylvesterType Quaternion Matrix Equations
 Hessenberg Matrices: Their Properties and Some Applications
 Equivalence of Polynomial Matrices over a Field
 Matrices in Chemical Problems Modeled Using Directed Graphs and Multigraphs
 Engaging Students in the Learning of Linear Algebra
 Index.
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 Rosén, Andreas, author.
 Cham, Switzerland : Birkhäuser, 2019.
 Description
 Book — 1 online resource (xiii, 465 pages) : illustrations (some color)
 Summary

 Prelude: Linear algebra
 Exterior algebra
 Clifford algebra
 Mappings of inner product spaces
 Spinors in inner product spaces
 Interlude: Analysis
 Exterior calculus
 Hodge decompositions
 Hypercomplex analysis
 Dirac equations
 Multivector calculus on manifolds
 Two index theorems.
5. Concise Introduction to Linear Algebra [2018]
 Hu, Qingwen, author.
 First edition.  Boca Raton, FL : CRC Press, Taylor & Francis Group, [2018]
 Description
 Book — 1 online resource.
 Summary

 Vectors and linear systems. Solving linear systems. Vector spaces. Orthogonality. Determinants. Eigenvalues and Eigenvectors. Singular value decomposition.
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 Online

 EBSCOhost Access limited to 3 simultaneous users
 Google Books (Full view)
6. Introduction to linear algebra [2002]
 Johnson, Lee W.
 Fifth edition.  New York, NY : Pearson, [2018]
 Description
 Book — 1 volume (various pagings) : illustrations (some color) ; 24 cm.
 Summary

For courses in introductory linear algebra This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit www.pearsonhighered.com/mathclassicsseries for a complete list of titles. Introduction to Linear Algebra, 5th Edition is a foundation book that bridges both practical computation and theoretical principles. Due to its flexible table of contents, the book is accessible for both students majoring in the scientific, engineering, and social sciences, as well as students that want an introduction to mathematical abstraction and logical reasoning. In order to achieve the text's flexibility, the book centers on 3 principal topics: matrix theory and systems of linear equations, elementary vector space concepts, and the eigenvalue problem. This highly adaptable text can be used for a onequarter or onesemester course at the sophomore/junior level, or for a more advanced class at the junior/senior level.
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QA184.2 .J63 2018  Unknown 
7. Linear algebra [2018]
 Nair, M. Thamban, author.
 Singapore : Springer, 2018.
 Description
 Book — 1 online resource (xi, 341 pages) : illustrations Digital: text file.PDF.
 Summary

 Chapter 1. Vector Spaces.
 Chapter 2. Linear Transformations.
 Chapter 3. Elementary Operations.
 Chapter 4. Inner Product Spaces.
 Chapter 5. Eigenvalues and Eigenvectors.
 Chapter 6. Block Diagonal Representation.
 Chapter 7. Spectral Decomposition.
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8. Linear algebra [2017]
 SaidHouari, Belkacem, author.
 Cham, Switzerland : Birkḧauser, 2017.
 Description
 Book — xiii, 384 pages ; 24 cm.
 Summary

 Matrices and matrix operations. Determinants. General vector spaces. Linear transformations. Linear transformations and matrices. Eigenvalues and eigenvectors. Orthogonal matrices and quadratic forms.
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QA184.2 .S253 2017  Unknown 
 Beilina, Larisa, author.
 Cham, Switzerland : Springer, [2017]
 Description
 Book — xiv, 450 pages ; 25 cm
 Summary

This book combines a solid theoretical background in linear algebra with practical algorithms for numerical solution of linear algebra problems. Developed from a number of courses taught repeatedly by the authors, the material covers topics like matrix algebra, theory for linear systems of equations, spectral theory, vector and matrix norms combined with main direct and iterative numerical methods, least squares problems, and eigenproblems. Numerical algorithms illustrated by computer programs written in MATLAB (R) are also provided as supplementary material on SpringerLink to give the reader a better understanding of professional numerical software for the solution of reallife problems. Perfect for a one or twosemester course on numerical linear algebra, matrix computation, and large sparse matrices, this text will interest students at the advanced undergraduate or graduate level.
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QA184.2 .B45 2017  CHECKEDOUT Request 
10. First Course in Linear Algebra [2016]
 Bhattacharya, P.B.
 [Place of publication not identified] : New Age International : NEW AGE International Publishers, 2016.
 Description
 Book — 1 online resource.
11. Advanced linear algebra [2015]
 Cooperstein, Bruce, 1950
 2nd ed.  Boca Raton : CRC Press, c2015.
 Description
 Book — xxvii, 594 p. : ill. ; 24 cm.
 Summary

 Preface to the Second Edition Preface to the First Edition Acknowledgments List of Figures Symbol Description Vector Spaces Fields The Space n Vector Spaces over an Arbitrary Field Subspaces of Vector Spaces Span and Independence Bases and FiniteDimensional Vector Spaces Bases and InfiniteDimensional Vector Spaces Coordinate Vectors Linear Transformations Introduction to Linear Transformations The Range and Kernel of a Linear Transformation The Correspondence and Isomorphism Theorems Matrix of a Linear Transformation The Algebra of L(V, W) and Mmn( ) Invertible Transformations and Matrices Polynomials The Algebra of Polynomials Roots of Polynomials Theory of a Single Linear Operator Invariant Subspaces of an Operator Cyclic Operators Maximal Vectors Indecomposable Linear Operators Invariant Factors and Elementary Divisors Canonical Forms Operators on Real and Complex Vector Spaces Normed and Inner Product Spaces Inner Products Geometry in Inner Product Spaces Orthonormal Sets and the GramSchmidt Process Orthogonal Complements and Projections Dual Spaces Adjoints Normed Vector Spaces Linear Operators on Inner Product Spaces SelfAdjoint and Normal Operators Spectral Theorems Normal Operators on Real Inner Product Spaces Unitary and Orthogonal Operators The Polar Decomposition and Singular Value Decomposition Trace and Determinant of a Linear Operator Trace of a Linear Operator Determinant of a Linear Operator and Matrix Uniqueness of the Determinant of a Linear Operator Bilinear Forms Basic Properties of Bilinear Maps Symplectic Spaces Quadratic Forms and Orthogonal Space Orthogonal Space, Characteristic Two Real Quadratic Forms Sesquilinear Forms and Unitary Geometry Basic Properties of Sesquilinear Forms Unitary Space Tensor Products Introduction to Tensor Products Properties of Tensor Products The Tensor Algebra The Symmetric Algebra The Exterior Algebra Clifford Algebras, char <> 2 Linear Groups and Groups of Isometries Linear Groups Symplectic Groups Orthogonal Groups, char <> 2 Unitary Groups Additional Topics in Linear Algebra Matrix Norms The MoorePenrose Inverse of a Matrix Nonnegative Matrices The Location of Eigenvalues Functions of Matrices Applications of Linear Algebra Least Squares Error Correcting Codes Ranking Webpages for Search Engines Appendices Concepts from Topology and Analysis Concepts from Group Theory Answers to Selected Exercises Hints to Selected Problems Bibliography Index.
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Advanced Linear Algebra, Second Edition takes a gentle approach that starts with familiar concepts and then gradually builds to deeper results. Each section begins with an outline of previously introduced concepts and results necessary for mastering the new material. By reviewing what students need to know before moving forward, the text builds a solid foundation upon which to progress. The new edition of this successful text focuses on vector spaces and the maps between them that preserve their structure (linear transformations). Designed for advanced undergraduate and beginning graduate students, the book discusses the structure theory of an operator, various topics on inner product spaces, and the trace and determinant functions of a linear operator. It addresses bilinear forms with a full treatment of symplectic spaces and orthogonal spaces, as well as explains the construction of tensor, symmetric, and exterior algebras. Featuring updates and revisions throughout, Advanced Linear Algebra, Second Edition: * Contains new chapters covering sesquilinear forms, linear groups and groups of isometries, matrices, and three important applications of linear algebra * Adds sections on normed vector spaces, orthogonal spaces over perfect fields of characteristic two, and Clifford algebras * Includes several new exercises and examples, with a solutions manual available upon qualifying course adoption The book shows students the beauty of linear algebra while preparing them for further study in mathematics.
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QA184.2 .C66 2015  Unknown 
12. Linear algebra done right [2015]
 Axler, Sheldon Jay.
 Third edition.  Cham : Springer, [2015]
 Description
 Book — xvii, 340 pages : ill. ; 25 cm.
 Summary

 Preface for the InstructorPreface for the StudentAcknowledgments
 1. Vector Spaces
 2. FiniteDimensional Vector Spaces
 3. Linear Maps
 4. Polynomials
 5. Eigenvalues, Eigenvectors, and Invariant Subspaces
 6. Inner Product Spaces
 7. Operators on Inner Product Spaces
 8. Operators on Complex Vector Spaces
 9. Operators on Real Vector Spaces
 10. Trace and DeterminantPhoto CreditsSymbol IndexIndex.
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On reserve: Ask at Science circulation desk  
QA184 .A96 2015  Unknown 2hour loan 
Stacks  
QA184 .A96 2015  CHECKEDOUT 
MATH5601
 Course
 MATH5601  Proofs and Modern Mathematics
 Instructor(s)
 Andras Vasy
13. Álgebra lineal [2014]
 Martínez, Héctor Jairo, author.
 Primera edición.  Santiago de Cali : Programa Editorial Universidad del Valle, 2014.
 Description
 Book — 1 online resource (407 pages)
14. Linear algebra with applications [2014]
 Baker, R. C. (Roger Clive), 1947 author.
 New Jersey : World Scientific, [2014]
 Description
 Book — ix, 320 pages : illustrations ; 26 cm
 Online
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QA184.2 .B354 2014  Unknown 
 Bronson, Richard author.
 Third edition / Richard Bronson, Gabriel Costa, John T. Saccoman.  Amsterdam : Academic Press, 2013.
 Description
 Book — 1 online resource.
 Summary

In this appealing and wellwritten text, Richard Bronson starts with the concrete and computational, and leads the reader to a choice of major applications. The first three chapters address the basics: matrices, vector spaces, and linear transformations. The next three cover eigenvalues, Euclidean inner products, and Jordan canonical forms, offering possibilities that can be tailored to the instructor's taste and to the length of the course. Bronson's approach to computation is modern and algorithmic, and his theory is clean and straightforward. Throughout, the views of the theory presented are broad and balanced and key material is highlighted in the text and summarized at the end of each chapter. The book also includes ample exercises with answers and hints. Prerequisite: One year of calculus is recommended. * Introduces deductive reasoning and helps the reader develop a facility with mathematical proofs* Provides a balanced approach to computation and theory by offering computational algorithms for finding eigenvalues and eigenvectors * Offers excellent exercise sets, ranging from drill to theoretical/challeging along with useful and interesting applications not found in other introductory linear algebra texts.
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16. Linear algebra in action [2013]
 Dym, H. (Harry), 1938 author.
 Second edition.  Providence, Rhode Island : American Mathematical Society, [2013]
 Description
 Book — xix, 585 pages : illustrations ; 26 cm.
 Summary

Linear algebra permeates mathematics, perhaps more so than any other single subject. It plays an essential role in pure and applied mathematics, statistics, computer science, and many aspects of physics and engineering. This book conveys in a userfriendly way the basic and advanced techniques of linear algebra from the point of view of a working analyst. The techniques are illustrated by a wide sample of applications and examples that are chosen to highlight the tools of the trade. In short, this is material that many of us wish we had been taught as a graduate student. Roughly the first third of the book covers the basic material of a first course in linear algebra. The remaining chapters are devoted to applications drawn from vector calculus, numerical analysis, control theory, complex analysis, convexity and functional analysis. In particular, fixed point theorems, extremal problems, matrix equations, zero location and eigenvalue location problems, and matrices with nonnegative entries are discussed. Appendices on useful facts from analysis and supplementary information from complex function theory are also provided for the convenience of the reader. In this new edition, most of the chapters in the first edition have been revised, some extensively. The revisions include changes in a number of proofs, either to simplify the argument, to make the logic clearer or, on occasion, to sharpen the result. New introductory sections on linear programming, extreme points for polyhedra and a NevanlinnaPick interpolation problem have been added, as have some very short introductory sections on the mathematics behind Google, Drazin inverses, band inverses and applications of SVD together with a number of new exercises.
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QA184.2 .D96 2013  Unknown 
17. Álgebra lineal [2012]
 García Jaimes, Orlando, author.
 Medellín, Colombia : Fondo Editorial Universidad EAFIT, [2012]
 Description
 Book — 1 online resource : illustrations Digital: text file.PDF.
 O'Meara, Kevin C.
 Oxford ; New York : Oxford University Press, c2011.
 Description
 Book — xxii, 400 p. : ill. ; 25 cm.
 Summary

 Preface
 Chapter 1. Background Linear Algebra
 Chapter 2. The Weyr Form
 Chapter 3. Centralizers
 Chapter 4. The Module Setting
 Chapter 5. Gerstenhaber's Theorem
 Chapter 6. Approximate Simultaneous Diagonalization
 Chapter 7. Algebraic Varieties
 Bibliography.
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 Online
Science Library (Li and Ma)
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QA184.2 .O44 2011  Unknown 
19. Álgebra lineal [electronic resource] [2011]
 Gutiérez García, Ismael.
 2. ed.  Barranquilla, Colombia : Editorial Universidad del Norte, 2011. (New York, NY. : Digitalia Inc, 2012)
 Description
 Book — 1 online resource (vii, 205 p.) : ill.
20. Álgebra Linear para todos [2011]
 Algera lineare per tutti. Spanish
 Robbiano, Lorenzo.
 Milan : Springer, ©2011.
 Description
 Book — 1 online resource (xvii, 213 pages)
 Summary

 Title Page; Copyright Page; Prefácio; Introdução; Table of Contents; Cálculo numérico e cálculo simbólico; Equação ax = b. Tentemos resolvêla; Equação ax = b. Atenção aos erros; Equação ax = b. Manipulemos os símbolos; Exercícios; Parte I; 1 Sistemas lineares e matrizes; 1.1 Exemplos de Sistemas Lineares; 1.2 Vetores e matrizes; 1.3 Sistemas lineares genéricos e matrizes associadas; 1.4 Formalismo Ax = b; Exercícios; 2 Operações com matrizes; 2.1 Soma e produto por um número; 2.2 Produto linha por coluna; 2.3 Quanto custa multiplicar duas matrizes?
 2.4 Algumas propriedades do produto de matrizes2.5 Inversa de uma matriz; Exercícios; 3 Solução dos Sistemas Lineares; 3.1 Matrizes elementares; 3.2 Sistemas lineares quadrados, o método de Gauss; 3.3 Cálculo efetivo da inversa; 3.4 Quanto custa o método de Gauss?; 3.5 Decomposição LU; 3.6 Método de Gauss para sistemas gerais; 3.7 Determinantes; Exercícios; 4 Sistema de coordenadas; 4.1 Escalares e Vetores; 4.2 Coordenadas cartesianas; 4.3 A regra do paralelogramo; 4.4 Sistemas ortogonais, áreas, determinantes; 4.5 Ângulos, módulos, produtos escalares.
 4.6 Produtos escalares e determinantes em geral4.7 Mudança de coordenadas; 4.8 Espaços vetoriais e bases; Exercícios; Parte II; 5 Formas quadráticas; 5.1 Equações de segundo grau; 5.2 Operações elementares sobre matrizes simétricas; 5.3 Formas quadráticas, funções e positividade; 5.4 Decomposição de Cholesky; Exercícios; 6 Ortogonalidade e ortonormalidade; 6.1 Uplas ortonormais e matrizes ortonormais; 6.2 Rotações; 6.3 Subespaços, independência linear, posto, dimensão; 6.4 Bases ortonormais e GramSchmidt; 6.5 Decomposição QR; Exercícios; 7 Projetores, pseudoinversa, mínimos quadrados.
 7.1 Matrizes e transformações lineares7.2 Projetores; 7.3 Mínimos quadrados e pseudoinversas; Exercícios; 8 Endomorfismos e diagonalização; 8.1 Um exemplo de transformação linear plana; 8.2 Autovalores, autovetores, autoespaços, semelhança; 8.3 Potência de matrizes; 8.4 Os coelhos de Fibonacci; 8.5 Sistemas diferenciais; 8.6 Diagonabilidade das matrizes simétricas reais; Exercícios; Parte III; Apêndice; Conclusão?; Referências; Índice Remissivo.
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