Автор: Radoslaw Pytlak Название: Conjugate Gradient Algorithms in Nonconvex Optimization ISBN: 3642099254 ISBN-13(EAN): 9783642099250 Издательство: Springer Рейтинг: Цена: 27251.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book details algorithms for large-scale unconstrained and bound constrained optimization. It shows optimization techniques from a conjugate gradient algorithm perspective as well as methods of shortest residuals, which have been developed by the author.
Автор: M.R. Hestenes Название: Conjugate Direction Methods in Optimization ISBN: 1461260507 ISBN-13(EAN): 9781461260509 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Shortly after the end of World War II high-speed digital computing machines were being developed. We discovered, for example, that even Gaus- sian elimination was not well understood from a machine point of view and that no effective machine oriented elimination algorithm had been developed.
Автор: M.R. Hestenes Название: Conjugate Direction Methods in Optimization ISBN: 0387904557 ISBN-13(EAN): 9780387904559 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Shortly after the end of World War II high-speed digital computing machines were being developed. We discovered, for example, that even Gaus- sian elimination was not well understood from a machine point of view and that no effective machine oriented elimination algorithm had been developed.
Автор: Artur Lemonte Название: The Gradient Test ISBN: 012803596X ISBN-13(EAN): 9780128035962 Издательство: Elsevier Science Рейтинг: Цена: 8757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The Gradient Test: Another Likelihood-Based Test presents the latest on the gradient test, a large-sample test that was introduced in statistics literature by George R. Terrell in 2002. The test has been studied by several authors, is simply computed, and can be an interesting alternative to the classical large-sample tests, namely, the likelihood ratio (LR), Wald (W), and Rao score (S) tests.
Due to the large literature about the LR, W and S tests, the gradient test is not frequently used to test hypothesis. The book covers topics on the local power of the gradient test, the Bartlett-corrected gradient statistic, the gradient statistic under model misspecification, and the robust gradient-type bounded-influence test.
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