MM Optimization Algorithms (Hardcover)

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.

General

Imprint:	Society For Industrial & Applied Mathematics,U.S.
Country of origin:	United States
Release date:	July 2016
Authors:	Kenneth Lange
Dimensions:	229 x 152 x 22mm (L x W x T)
Format:	Hardcover
Pages:	232
ISBN-13:	978-1-61197-439-3
Categories:	Books > Science & Mathematics > Mathematics > Optimization > General Books > Science & Mathematics > Mathematics > Applied mathematics > General Promotions
LSN:	1-61197-439-9
Barcode:	9781611974393

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