MM Optimization Algorithms
Seiten
2016
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-1-61197-439-3 (ISBN)
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-1-61197-439-3 (ISBN)
Presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics. The author derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining.
Offers an overview of the MM principle, a device for deriving optimization algorithms satisfying the ascent or descent property. These algorithms can:
Separate the variables of a problem.
Avoid large matrix inversions.
Linearize a problem.
Restore symmetry.
Deal with equality and inequality constraints gracefully.
Turn a non-differentiable problem into a smooth problem.
The author:
Presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics.
Derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining.
Summarizes a large amount of literature that has not reached book form before.
Offers an overview of the MM principle, a device for deriving optimization algorithms satisfying the ascent or descent property. These algorithms can:
Separate the variables of a problem.
Avoid large matrix inversions.
Linearize a problem.
Restore symmetry.
Deal with equality and inequality constraints gracefully.
Turn a non-differentiable problem into a smooth problem.
The author:
Presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics.
Derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining.
Summarizes a large amount of literature that has not reached book form before.
Chapter 1: Beginning Examples
Chapter 2: Convexity and Inequalities
Chapter 3: Nonsmooth Analysis
Chapter 4: Majorization and Minorization
Chapter 5: Proximal Algorithms
Chapter 6: Regression and Multivariate Analysis
Chapter 7: Convergence and Acceleration
Appendix A: Mathematical Background
| Erscheinungsdatum | 06.04.2017 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Gewicht | 695 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| ISBN-10 | 1-61197-439-9 / 1611974399 |
| ISBN-13 | 978-1-61197-439-3 / 9781611974393 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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