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Transform Linear Algebra(2002) 요약정보 및 구매

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지은이 Uhlig
발행년도 2002-04-10
판수 1판
페이지 528
ISBN 9780130415356
도서상태 구매가능
판매가격 5,000원
포인트 0점
배송비결제 주문시 결제

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  • Transform Linear Algebra(2002)
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관련상품

  • This book encourages readers to develop an intuitive understanding of the foundations of Linear Algebra. An emphasis on the concepts of Linear Algebra and Matrix Theory conveys the structure and nature of Linear Spaces and of Linear Transformations. Almost every chapter has three sections: a lecture followed by problems, theoretical and mathematical enrichment, and applications to and from Linear Algebra. Specific chapter topics cover linear transformations; row reduction; linear equations; subspaces; linear dependence, bases, and dimension; composition of maps, matrix inverse and transpose; coordinate vectors, basis change; determinants, …l-matrices; matrix eigenvalues; orthogonal bases and orthogonal matrices; symmetric and normal matrix eigenvalues; singular values; and basic numerical linear algebra techniques. For individuals in fields related to economics, engineering, science, or mathematics.

  • Introduction (Mathematical Preliminaries, Vectors, Sets, and Symbols).


    1. Linear Transformations.

    Lecture One: Vectors, Linear Functions, and Matrices. Tasks and Methods of Linear Algebra. Applications: Geometry, Calculus, and MATLAB.


    2. Row-Reduction.

    Lecture Two: Gaussian Elimination and the Echelon Forms. Applications: MATLAB.


    3. Linear Equations.

    Lecture Three: Solvability and Solutions of Linear Systems. Applications: Circuits, Networks, Chemistry, and MATLAB.


    4. Subspaces.

    Lecture Four: The Image and Kernel of a Linear Transformation. Applications: Join and Intersection of Subspaces.


    5. Linear Dependence, Bases, and Dimension.

    Lecture Five: Minimal Spanning or Maximally Independent Sets of Vectors. Applications: Multiple Spanning Sets of One Subspace, MATLAB.


    6. Composition of Maps, Matrix Inverse.

    Lecture Six. Theory: Gauss Elimination Matrix Products, the Uniqueness of the Inverse, and Block Matrix Products. Applications (MATLAB).


    7. Coordinate Vectors, Basis Change.

    Lecture Seven: Matrix Representations with Respect to General Bases. Theory: Rank, Matrix Transpose. Applications: Subspace Basis Change, Calculus.


    8. Determinants, Lambda-Matrices.

    Lecture Eight: Laplace Expansion, Gaussian Elimination, and Properties. Theory: Axiomatic Definition. Applications: Volume Wronskian.


    9. Matrix Eigenvalues and Eigenvectors.

    Lecture Nine, Using Vector Iteration: Vanishing and Minimal Polynomial, Matrix Eigenanalysis, and Diagonalizable Matrices. Lecture Nine, Using Determinants: Characteristic Polynomial, Matrix Eigenanalysis, and Diagonalizable Matrices. Theory: Geometry, Vector Iteration, and Eigenvalue Functions. Applications: Stochastic Matrices, Systems of Linear DE's and MATLAB.


    10. Orthogonal Bases and Orthogonal Matrices.

    Lecture Ten: Length, Orthogonality, and Orthonormal Bases. Theory: Matrix Generation, Rank 1 and Householder Matrices. Applications: QR Decomposition, MATLAB, and Least Squares.


    11. Symmetric and Normal Matrix Eigenvalues.

    Lecture Eleven: Matrix Representations with respect to One Orthonormal Basis. Theory: Normal Matrices. Applications: Polar Decomposition, Volume, ODEs, and Quadrics.


    12. Singular Values.

    Lecture Twelve: Matrix Representations w.r.t. Two Orthonormal Bases. Theory: Matrix Approximation, Least Squares. Applications: Geometry, Data Compression, Least Squares, and MATLAB.


    13. Basic Numerical Linear Algebra Techniques.

    Lecture Thirteen: Computer Arithmetic, Stability, and the QR Algorithm.


    14. Nondiagonalizable Matrices, the Jordan Normal Form.

    Lecture Fourteen: (Jordan Normal Form). Theory: Real Jordan Normal Form, Companion Matrix. Applications: Linear Differential Equations, Positive Matrices.


    Epilogue.


    Appendix A (Complex Numbers and Vectors).


    Appendix B (Finding Integer Roots of Integer Polynomials).


    Appendix C (Abstract Vector Spaces).


    *Appendix D (Inner Product Spaces).


    Solutions.


    Index.


    List of Photographs.

  • Frank Uhlig. Born April 2, 1945, Mägdesprung/Harz

    grew up in Mülheim/Ruhr, Germany

    married, two sons. Mathematics student at University of Cologne, California Institute of Technology. Ph.D., CalTech, 1972

     Assistant, University of Würzburg, RWTH Aachen, Germany, 1972-1982

    Two Habilitations (Mathematics), University of Würzburg 1977, RWTH Aachen 1978

    Visiting Professor, Oregon State University 1979/1980

    Professor of Mathematics, Auburn University 1982

    Two Fulbright Grants

    (Co-)organizer of eight research conferences. 

    Research Areas: linear algebra, matrix theory, numerical analysis, numerical algebra, geometry, Krein spaces, graph theory, mechanics, inverse problems. 40+ papers, 2+ books.

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  • Transform Linear Algebra(2002)
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