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.
