
MM Optimization Algorithms
Kenneth Lange(Author)
Society for Industrial & Applied Mathematics,U.S. (Publisher)
Published on 30. July 2016
Book
Hardback
232 pages
978-1-61197-439-3 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Dimensions
Height: 229 mm
Width: 152 mm
Weight
695 gr
ISBN-13
978-1-61197-439-3 (9781611974393)
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Schweitzer Classification
Content
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
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