MM Optimization Algorithms

MM Optimization Algorithms 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, and
turn a nondifferentiable 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; and
summarizes a large amount of literature that has not reached book form before.