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.
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