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Conjugate Gradient Algorithms in Nonconvex Optimization - Radoslaw Pytlak

Conjugate Gradient Algorithms in Nonconvex Optimization

By: Radoslaw Pytlak

eText | 18 November 2008

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Conjugate Direction Methods were proposed in the early 1950s. With the development of our full computers, attempts were made to lie foundations for mathematical aspects of computations which could take advantage of these computers. This monograph gives an overview of the standard conjugate gradient algorithms, but it goes beyond the treatment of technics and it can therefore be regarded as an extension to the methods originally proposed. The book draws much attention to the preconditioned versions of the method and since limited memory quasi-Newton algorithms are preconditioned conjugate gradient algorithms when apply to quadratics, these variable metric techniques are also discussed.
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