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.
- ISBN13 9781611974393
- Publish Date 30 July 2016
- Publish Status Active
- Publish Country US
- Imprint Society for Industrial & Applied Mathematics,U.S.
- Format Hardcover
- Pages 232
- Language English