Synopsis
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.
"synopsis" may belong to another edition of this title.
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MM Optimization Algorithms (eng)
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MM OPTIMIZATION ALGORITHMS (OT147)
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Hardcover. Condition: new. 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. Presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics. The author derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781611974393