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.
