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, ... Read More
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. Read Less
Add this copy of Mm Optimization Algorithms to cart. $77.99, very good condition, Sold by FirstClassBooks rated 4.0 out of 5 stars, ships from Little Rock, AR, UNITED STATES, published 2016 by Society for Industrial & Applied Mathematics,U.S..
Edition:
2016, Society for Industrial & Applied Mathematics,U.S.
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Edition:
2016, Society for Industrial & Applied Mathematics,U.S.
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Edition:
2016, Society for Industrial & Applied Mathematics,U.S.
