Address vector and matrix methods necessary in numerical methods and optimization of linear systems with this practical, unified text. Perfect for advanced undergraduate students and graduate students in engineering, physical sciences, and applied mathematics.
Address vector and matrix methods necessary in numerical methods and optimization of linear systems with this practical, unified text. Perfect for advanced undergraduate students and graduate students in engineering, physical sciences, and applied mathematics.
Kevin W. Cassel is Professor of Mechanical and Aerospace Engineering and Professor of Applied Mathematics at the Illinois Institute of Technology. He is also an Associate Fellow of the American Institute of Aeronautics and Astronautics.
Inhaltsangabe
Part I. Matrix Methods: 1. Vector and matrix algebra 2. Algebraic eigenproblems and their applications 3. Differential eigenproblems and their applications 4. Vector and matrix calculus 5. Analysis of discrete dynamical systems Part II. Numerical Methods: 6. Computational linear algebra 7. Numerical methods for differential equations 8. Finite-difference methods for boundary-value problems 9. Finite-difference methods for initial-value problems Part III. Least Squares and Optimization: 10. Least-squares methods 11. Data analysis - curve fitting and interpolation 12. Optimization and root finding of algebraic systems 13. Data-driven methods and reduced-order modeling.
Part I. Matrix Methods: 1. Vector and matrix algebra 2. Algebraic eigenproblems and their applications 3. Differential eigenproblems and their applications 4. Vector and matrix calculus 5. Analysis of discrete dynamical systems Part II. Numerical Methods: 6. Computational linear algebra 7. Numerical methods for differential equations 8. Finite-difference methods for boundary-value problems 9. Finite-difference methods for initial-value problems Part III. Least Squares and Optimization: 10. Least-squares methods 11. Data analysis - curve fitting and interpolation 12. Optimization and root finding of algebraic systems 13. Data-driven methods and reduced-order modeling.
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