This third edition of the successful outline in linear algebra--which sold more than 400,000 copies in its past two editions--has been thoroughly updated to increase its applicability to the fields in which linear algebra is now essential: computer science, engineering, mathematics, physics, and quantitative analysis. Revised coverage includes new problems relevant to computer science and a revised chapter on linear equations.
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Students love Schaum’s Outlines because they produce results. Each year, hundreds of thousands of students improve their test scores and final grades with these indispensable study guides.
Get the edge on your classmates. Use Schaum’s!
If you don't have a lot of time but want to excel in class, this book helps you:
* Use detailed examples to solve problems * Brush up before tests * Find answers fast * Study quickly and more effectively * Get the big picture without poring over lengthy textbooks
Schaum’s Outlines give you the information your teachers expect you to know in a handy and succinct format—without overwhelming you with unnecessary jargon. You get a complete overview of the subject. Plus, you get plenty of practice exercises to test your skill. Compatible with any classroom text, Schaum’s let you study at your own pace and remind you of all the important facts you need to remember—fast! And Schaum’s are so complete, they’re perfect for preparing for graduate or professional exams.
Inside, you will find: * A bridge between computational calculus and formal mathematics * Clear explanations of eigenvalues, eigenvectors, linear transformations, linear equations, vectors, and matrices * Solved problems that relate to the field you are studying * Easy-to-understand information, perfect for pre-test review
If you want top grades and a thorough understanding of linear algebra, this powerful study tool is the best tutor you can have!
Chapters include: Vectors in Rn and Cn * Matrix Algebra * Linear Equations * Vector Spaces * Linear Mappings * Linear Mapings and Matrices * Inner Product Spaces, Orthogonality * Determinants * Diagonalization: Eigenvalues and Eigenvectors * Canonical Forms * Linear Functionals and the Dual Space * Bilinear, Quadratic, and Hermitian Forms * Linear Operators on Inner Product Spaces * Polynomials
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