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.
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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.
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.
Detaljer
- Forlag
- Society for Industrial & Applied Mathematics,U.S.
- Innbinding
- Innbundet
- Språk
- Engelsk
- ISBN
- 9781611974393
- Utgivelsesår
- 2016
- Format
- 23 x 15 cm
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Bøker som ligner på MM Optimization Algorithms:
Introduction to Optimization with Applications in Machine Learning and Data Analytics
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