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.
- ISBN:
- 9781611974393
- 9781611974393
-
Category:
- Mathematics
- Format:
- Hardback
- Publication Date:
-
30-07-2016
- Language:
- English
- Publisher:
- Society for Industrial and Applied Mathematics
- Country of origin:
- United States
- Dimensions (mm):
- 229x152mm
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