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Zusatztext "In brief! I think the book is wonderful! ? It's pedagogically excellent ? The examples are very thoughtfully chosen! anticipating possible misunderstandings and illuminating both the main idea and some subtleties ? The emphasis on recursion is unusual and valuable ? It's rich in topics not often! if at all! treated effectively in texts at this level ? Again! I think this book is terrific ?"-Harriet Pollatsek! Professor of Mathematics and Julia and Sarah Ann Adams Professor of Science! Mount Holyoke College! South Hadley! Massachusetts! USA Informationen zum Autor Robert A. Liebler Klappentext Classroom tested with great success, this text builds the foundation in matrix algebra needed by freshman students in mathematics, physics, and computer science and by upper-level business and social science students. The author presents the material with outstanding pedagogical clarity, many figures and diagrams, and interrelated examples. The presentation relies heavily on the use of a graphing calculator, includes section summaries, and provides exercises in each chapter. Organized into bite-sized objectives, this relatively gentle treatment prepares readers well for the advanced studies their fields will require. Supporting material, including Maple worksheets, are available for download from the Internet. Zusammenfassung Builds the foundation in matrix algebra needed by freshman students in mathematics, physics, and computer science and by upper-level business and social science students. This book presents the material with pedagogical clarity, many figures and diagrams, and interrelated examples. Inhaltsverzeichnis SYSTEMS OF LINEAR EQUATIONS AND THEIR SOLUTION: Recognizing Linear Systems and Solutions. Matrices, Equivalence and Row Operations. Echelon Forms and Gaussian Elimination. Free Variables and General Solutions. The Vector Form of the General Solution. Geometric Vectors and Linear Functions. Polynomial Interpolation. MATRIX NUMBER SYSTEMS: Complex Numbers. Matrix Multiplication. Auxiliary Matrices and Matrix Inverses . Symmetric Projectors, Resolving Vectors . Least Squares Approximation. Changing Plane Coordinates. The Fast Fourier Transform and the Euclidean Algorithm. DIAGONALIZABLE MATRICES: Eigenvectors and Eigenvalues. The Minimal Polynomial Algorithm. Linear Recurrence Relations. Properties of the Minimal Polynomial. The Sequence {Ak}. Discrete Dynamical Systems. Matrix Compression with ComponentsDETERMINANTS: Area and Composition of Linear Functions. Computing Determinants. Fundamental Properties of Determinants . Further Applications. APPENDIX. SELECTED PRACTICE PROBLEM ANSWERS. INDEX....
