Provides a comprehensive picture of model reduction, combining system theory with numerical linear algebra and computational considerations. It addresses the issue of model reduction and the resulting trade-offs between accuracy and complexity. Special attention is given to numerical aspects, simulation questions, and practical applications.
Provides a comprehensive picture of model reduction, combining system theory with numerical linear algebra and computational considerations. It addresses the issue of model reduction and the resulting trade-offs between accuracy and complexity. Special attention is given to numerical aspects, simulation questions, and practical applications.
1. List of Figures 2. Foreword 3. Preface 4. How to Use this Book 5. Part I: Introduction. Chapter 1: Introduction 6. Chapter 2: Motivating Examples 7. Part II: Preliminaries. Chapter 3: Tools from Matrix Theory 8. Chapter 4: Linear Dynamical Systems: Part 1 9. Chapter 5: Linear Dynamical Systems: Part 2 10. Chapter 6: Sylvester and Lyapunov equations 11. Part III: SVD-based Approximation Methods. Chapter 7: Balancing and balanced approximations 12. Chapter 8: Hankel-norm Approximation 13. Chapter 9: Special topics in SVD-based approximation methods 14. Part IV: Krylov-based Approximation Methods 15. Chapter 10: Eigenvalue Computations 16. Chapter 11: Model Reduction Using Krylov Methods 17. Part V: SVD–Krylov Methods and Case Studies. Chapter 12: SVD–Krylov Methods 18. Chapter 13: Case Studies 19. Chapter 14: Epilogue 20. Chapter 15: Problems 21. Bibliography 22. Index.
1. List of Figures 2. Foreword 3. Preface 4. How to Use this Book 5. Part I: Introduction. Chapter 1: Introduction 6. Chapter 2: Motivating Examples 7. Part II: Preliminaries. Chapter 3: Tools from Matrix Theory 8. Chapter 4: Linear Dynamical Systems: Part 1 9. Chapter 5: Linear Dynamical Systems: Part 2 10. Chapter 6: Sylvester and Lyapunov equations 11. Part III: SVD-based Approximation Methods. Chapter 7: Balancing and balanced approximations 12. Chapter 8: Hankel-norm Approximation 13. Chapter 9: Special topics in SVD-based approximation methods 14. Part IV: Krylov-based Approximation Methods 15. Chapter 10: Eigenvalue Computations 16. Chapter 11: Model Reduction Using Krylov Methods 17. Part V: SVD–Krylov Methods and Case Studies. Chapter 12: SVD–Krylov Methods 18. Chapter 13: Case Studies 19. Chapter 14: Epilogue 20. Chapter 15: Problems 21. Bibliography 22. Index.
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