ALERT: Before you purchase, check with your instructor or review your course syllabus to ensure that you select the correct ISBN. Several versions of Pearson's MyLab & Mastering products exist for each title, including customized versions for individual schools, and registrations are not transferable. In addition, you may need a CourseID, provided by your instructor, to register for and use Pearson's MyLab & Mastering products. Packages Access codes for Pearson's MyLab & Mastering products may not be included when purchasing or renting from companies other than Pearson; check with the seller before completing your purchase. Used or rental books If you rent or purchase a used book with an access code, the access code may have been redeemed previously and you may have to purchase a new access code. Access codes Access codes that are purchased from sellers other than Pearson carry a higher risk of being either the wrong ISBN or a previously redeemed code. Check with the seller prior to purchase. --This package consists of the textbook plus an access kit for MyMathLab/MyStatLab. 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.NEW! MyMathLab is now available for this text. The course features hundreds of assignable homework exercises plus the complete eBook, in addition to its wide range of tutorial and assessment tools that make it easy to manage your course online.
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1. Linear Equations in Linear AlgebraIntroductory Example: Linear Models in Economics and Engineering1.1 Systems of Linear Equations1.2 Row Reduction and Echelon Forms1.3 Vector Equations1.4 The Matrix Equation Ax = b1.5 Solution Sets of Linear Systems1.6 Applications of Linear Systems1.7 Linear Independence1.8 Introduction to Linear Transformations1.9 The Matrix of a Linear Transformation1.10 Linear Models in Business, Science, and EngineeringSupplementary Exercises 2. Matrix AlgebraIntroductory Example: Computer Models in Aircraft Design2.1 Matrix Operations2.2 The Inverse of a Matrix2.3 Characterizations of Invertible Matrices2.4 Partitioned Matrices2.5 Matrix Factorizations2.6 The Leontief Input-Output Model2.7 Applications to Computer Graphics2.8 Subspaces of Rn2.9 Dimension and RankSupplementary Exercises 3. DeterminantsIntroductory Example: Random Paths and Distortion3.1 Introduction to Determinants3.2 Properties of Determinants3.3 Cramer's Rule, Volume, and Linear TransformationsSupplementary Exercises 4. Vector SpacesIntroductory Example: Space Flight and Control Systems4.1 Vector Spaces and Subspaces4.2 Null Spaces, Column Spaces, and Linear Transformations4.3 Linearly Independent Sets; Bases4.4 Coordinate Systems4.5 The Dimension of a Vector Space4.6 Rank4.7 Change of Basis4.8 Applications to Difference Equations4.9 Applications to Markov ChainsSupplementary Exercises 5. Eigenvalues and EigenvectorsIntroductory Example: Dynamical Systems and Spotted Owls5.1 Eigenvectors and Eigenvalues5.2 The Characteristic Equation5.3 Diagonalization5.4 Eigenvectors and Linear Transformations5.5 Complex Eigenvalues5.6 Discrete Dynamical Systems5.7 Applications to Differential Equations5.8 Iterative Estimates for EigenvaluesSupplementary Exercises 6. Orthogonality and Least SquaresIntroductory Example: Readjusting the North American Datum6.1 Inner Product, Length, and Orthogonality6.2 Orthogonal Sets6.3 Orthogonal Projections6.4 The Gram-Schmidt Process6.5 Least-Squares Problems6.6 Applications to Linear Models6.7 Inner Product Spaces6.8 Applications of Inner Product SpacesSupplementary Exercises 7. Symmetric Matrices and Quadratic FormsIntroductory Example: Multichannel Image Processing7.1 Diagonalization of Symmetric Matrices7.2 Quadratic Forms7.3 Constrained Optimization7.4 The Singular Value Decomposition7.5 Applications to Image Processing and StatisticsSupplementary Exercises 8. The Geometry of Vector SpacesIntroductory Example: The Platonic Solids8.1 Affine Combinations8.2 Affine Independence8.3 Convex Combinations8.4 Hyperplanes8.5 Polytopes8.6 Curves and Surfaces 9. Optimization (Online Only)Introductory Example: The Berlin Airlift9.1 Matrix Games9.2 Linear Programming-Geometric Method9.3 Linear Programming-Simplex Method9.4 Duality 10. Finite-State Markov Chains (Online Only)Introductory Example: Google and Markov Chains10.1 Introduction and Examples10.2 The Steady-State Vector and Google's PageRank10.3 Finite-State Markov Chains10.4 Classification of States and Periodicity10.5 The Fundamental Matrix10.6 Markov Chains and Baseball Statistics AppendicesA. Uniqueness of the Reduced Echelon FormB. Complex Numbers
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Produktdetaljer

ISBN
9780321399144
Publisert
2011-09-28
Utgave
4. utgave
Utgiver
Vendor
Pearson
Høyde
257 mm
Aldersnivå
05, UU
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
576

Forfatter

Biographical note

David C. Lay holds a B.A. from Aurora University (Illinois), and an M.A. and Ph.D. from the University of California at Los Angeles. Lay has been an educator and research mathematician since 1966, mostly at the University of Maryland, College Park. He has also served as a visiting professor at the University of Amsterdam, the Free University in Amsterdam, and the University of Kaiserslautern, Germany. He has over 30 research articles published in functional analysis and linear algebra.

As a founding member of the NSF-sponsored Linear Algebra Curriculum Study Group, Lay has been a leader in the current movement to modernize the linear algebra curriculum. Lay is also co-author of several mathematics texts, including Introduction to Functional Analysis, with Angus E. Taylor, Calculus and Its Applications, with L.J. Goldstein and D.I. Schneider, and Linear Algebra Gems-Assets for Undergraduate Mathematics, with D. Carlson, C.R. Johnson, and A.D. Porter.

Professor Lay has received four university awards for teaching excellence, including, in 1996, the title of Distinguished Scholar-Teacher of the University of Maryland. In 1994, he was given one of the Mathematical Association of America's Awards for Distinguished College or University Teaching of Mathematics. He has been elected by the university students to membership in Alpha Lambda Delta National Scholastic Honor Society and Golden Key National Honor Society. In 1989, Aurora University conferred on him the Outstanding Alumnus award. Lay is a member of the American Mathematical Society, the Canadian Mathematical Society, the International Linear Algebra Society, the Mathematical Association of America, Sigma Xi, and the Society for Industrial and Applied Mathematics. Since 1992, he has served several terms on the national board of the Association of Christians in the Mathematical Sciences.