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Nonlinear algebra in an ACORNwith ap...

Lee, Martin J.

Nonlinear algebra in an ACORNwith applications to deep learning /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Nonlinear algebra in an ACORNMartin J. Lee, Ken Tsang.
其他題名:	with applications to deep learning /
作者:	Lee, Martin J.
其他作者:	Tsang, Ken.
出版者:	Singapore :World Scientific,c2018.
面頁冊數:	1 online resource (92 p.) :ill. (some col.)
標題:	Robust optimization.
電子資源:	https://www.worldscientific.com/worldscibooks/10.1142/11022#t=toc
ISBN:	9789813271524$q(electronic bk.)

Nonlinear algebra in an ACORNwith applications to deep learning /
Lee, Martin J.

Nonlinear algebra in an ACORNwith applications to deep learning /[electronic resource] :Martin J. Lee, Ken Tsang. - 1st ed. - Singapore :World Scientific,c2018. - 1 online resource (92 p.) :ill. (some col.)

Includes bibliographical references (p. 71)

"A simple algorithm for solving a set of nonlinear equations by matrix algebra has been discovered recently — first by transforming them into an equivalent matrix equation and then finding the solution analytically in terms of the inverse matrix of this equation. With this newly developed ACORN (Adaptive Constrained Optimal Robust Nonlinear) algorithm, it is possible to minimize the objective function [constructed from the functions in the nonlinear set of equations] without computing its derivatives. This book will present the details of ACORN algorithm and how it is used to solve large scale nonlinear equations with an innovative approach ACORN Magic [minimization algorithms gathered in a cloud]. The ultimate motivation of this work is its application to optimization. In recent years, with the advances in big-data, optimization becomes an even more powerful tool in knowledge discovery. ACORN Magic is the perfect choice in this kind of application because of that fact that it is fast, robust and simple enough to be embedded in any type of machine learning program."--

Electronic reproduction.
Singapore :
World Scientific,
[2018]

Mode of access: World Wide Web.

ISBN: 9789813271524$q(electronic bk.)Subjects--Topical Terms:

747261
Robust optimization.

LC Class. No.: QA402.5 / .L44 2018

Dewey Class. No.: 519.72

Nonlinear algebra in an ACORNwith applications to deep learning /
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245 1 0 $a Nonlinear algebra in an ACORN $h [electronic resource] : $b with applications to deep learning / $c Martin J. Lee, Ken Tsang.
250 $a 1st ed.
260 $a Singapore : $b World Scientific, $c c2018.
300 $a 1 online resource (92 p.) : $b ill. (some col.)
504 $a Includes bibliographical references (p. 71)
520 $a "A simple algorithm for solving a set of nonlinear equations by matrix algebra has been discovered recently — first by transforming them into an equivalent matrix equation and then finding the solution analytically in terms of the inverse matrix of this equation. With this newly developed ACORN (Adaptive Constrained Optimal Robust Nonlinear) algorithm, it is possible to minimize the objective function [constructed from the functions in the nonlinear set of equations] without computing its derivatives. This book will present the details of ACORN algorithm and how it is used to solve large scale nonlinear equations with an innovative approach ACORN Magic [minimization algorithms gathered in a cloud]. The ultimate motivation of this work is its application to optimization. In recent years, with the advances in big-data, optimization becomes an even more powerful tool in knowledge discovery. ACORN Magic is the perfect choice in this kind of application because of that fact that it is fast, robust and simple enough to be embedded in any type of machine learning program."-- $c Publisher's website.
533 $a Electronic reproduction. $b Singapore : $c World Scientific, $d [2018]
538 $a Mode of access: World Wide Web.
588 $a Description based on online resource; title from PDF title page (viewed August 30, 2018)
650 0 $a Robust optimization. $3 747261
650 0 $a Mathematical optimization. $3 183292
650 0 $a Matrices. $3 181876
650 0 $a Electronic books. $3 242495
700 1 $a Tsang, Ken. $3 840206
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/11022#t=toc