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Englisch
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Beschreibung
Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.
Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.
Über den Autor
John Wright is an Associate Professor in the Electrical Engineering Department and the Data Science Institute at Columbia University.
Inhaltsverzeichnis
Foreword; Preface; Acknowledgements; 1. Introduction; Part I. Principles of Low-Dimensional Models: 2. Sparse Signal Models; 3. Convex Methods for Sparse Signal Recovery; 4. Convex Methods for Low-Rank Matrix Recovery; 5. Decomposing Low-Rank and Sparse Matrices; 6. Recovering General Low-Dimensional Models; 7. Nonconvex Methods for Low-Dimensional Models; Part II. Computation for Large-Scale Problems: 8. Convex Optimization for Structured Signal Recovery; 9. Nonconvex Optimization for High-Dimensional Problems; Part III. Applications to Real-World Problems: 10. Magnetic Resonance Imaging; 11. Wideband Spectrum Sensing; 12. Scientific Imaging Problems; 13. Robust Face Recognition; 14. Robust Photometric Stereo; 15. Structured Texture Recovery; 16. Deep Networks for Classification; Appendices: Appendix A. Facts from Linear Algebra and Matrix Analysis; Appendix B. Convex Sets and Functions; Appendix C. Optimization Problems and Optimality Conditions; Appendix D. Methods for Optimization; Appendix E. Facts from High-Dimensional Statistics; Bibliography; List of Symbols; Index.
Details
Erscheinungsjahr: | 2022 |
---|---|
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
Inhalt: | Gebunden |
ISBN-13: | 9781108489737 |
ISBN-10: | 1108489737 |
Sprache: | Englisch |
Einband: | Gebunden |
Autor: |
Wright, John
Ma, Yi |
Hersteller: | Cambridge University Press |
Maße: | 250 x 172 x 38 mm |
Von/Mit: | John Wright (u. a.) |
Erscheinungsdatum: | 13.01.2022 |
Gewicht: | 1,44 kg |
Über den Autor
John Wright is an Associate Professor in the Electrical Engineering Department and the Data Science Institute at Columbia University.
Inhaltsverzeichnis
Foreword; Preface; Acknowledgements; 1. Introduction; Part I. Principles of Low-Dimensional Models: 2. Sparse Signal Models; 3. Convex Methods for Sparse Signal Recovery; 4. Convex Methods for Low-Rank Matrix Recovery; 5. Decomposing Low-Rank and Sparse Matrices; 6. Recovering General Low-Dimensional Models; 7. Nonconvex Methods for Low-Dimensional Models; Part II. Computation for Large-Scale Problems: 8. Convex Optimization for Structured Signal Recovery; 9. Nonconvex Optimization for High-Dimensional Problems; Part III. Applications to Real-World Problems: 10. Magnetic Resonance Imaging; 11. Wideband Spectrum Sensing; 12. Scientific Imaging Problems; 13. Robust Face Recognition; 14. Robust Photometric Stereo; 15. Structured Texture Recovery; 16. Deep Networks for Classification; Appendices: Appendix A. Facts from Linear Algebra and Matrix Analysis; Appendix B. Convex Sets and Functions; Appendix C. Optimization Problems and Optimality Conditions; Appendix D. Methods for Optimization; Appendix E. Facts from High-Dimensional Statistics; Bibliography; List of Symbols; Index.
Details
Erscheinungsjahr: | 2022 |
---|---|
Genre: | Importe, Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Buch |
Inhalt: | Gebunden |
ISBN-13: | 9781108489737 |
ISBN-10: | 1108489737 |
Sprache: | Englisch |
Einband: | Gebunden |
Autor: |
Wright, John
Ma, Yi |
Hersteller: | Cambridge University Press |
Maße: | 250 x 172 x 38 mm |
Von/Mit: | John Wright (u. a.) |
Erscheinungsdatum: | 13.01.2022 |
Gewicht: | 1,44 kg |
Warnhinweis