Courses on linear algebra and numerical analysis need each other. Often NA courses have some linear algebra topics, and LA courses mention some topics from numerical analysis/scientific computing. This text merges these two areas into one introductory undergraduate course. It assumes students have had multivariable calculus.
Courses on linear algebra and numerical analysis need each other. Often NA courses have some linear algebra topics, and LA courses mention some topics from numerical analysis/scientific computing. This text merges these two areas into one introductory undergraduate course. It assumes students have had multivariable calculus.
Robert E. White is Professor Emeritus, North Carolina State University. He is also the author of Computational Mathematics: Models, Methods, Analysis with MATLAB® and MPI, second edition and Elements of Matrix Modeling and Computing with MATLAB®, both published by CRC Press.
Inhaltsangabe
1. Solution of AX = d. 2. Matrix Factorizations. 3. Least Squares and Normal Equations. 4. Ax = d with mEigenvectors and Orthonormal Basis. 7. Singular Value Decomposition. 8. Three Applications of SVD. 9. Pseudoinverse of A. 10. General Inner Product Vector Spaces. 11. Iterative Methods. 12. Nonlinear Problems and Least Squares.
1. Solution of AX = d. 2. Matrix Factorizations. 3. Least Squares and Normal Equations. 4. Ax = d with mEigenvectors and Orthonormal Basis. 7. Singular Value Decomposition. 8. Three Applications of SVD. 9. Pseudoinverse of A. 10. General Inner Product Vector Spaces. 11. Iterative Methods. 12. Nonlinear Problems and Least Squares.
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