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.
• Turn a non-differentiable problem into a smooth problem.
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    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.