Optimization algorithms von lange kenneth (20 Ergebnisse)

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Hardcover. Zustand: 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.…

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Zustand: New. 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 e.

Buch. Zustand: Neu. Neuware - 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.…

Hardback. Zustand: New.

Hardcover. Zustand: 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…