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Nonlinear algebra in an ACORN = with applications to deep learning /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Nonlinear algebra in an ACORN/ Martin J. Lee, Ken Tsang.
Reminder of title:	with applications to deep learning /
Author:	Lee, Martin J.
other author:	Tsang, Ken.
Published:	Singapore :World Scientific, : c2018.,
Description:	1 online resource (92 p.) :ill. (some col.) :
Subject:	Robust optimization. -
Online resource:	https://www.worldscientific.com/worldscibooks/10.1142/11022#t=toc
ISBN:	9789813271524

Nonlinear algebra in an ACORN = with 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: 9789813271524Subjects--Topical Terms:

745733
Robust optimization.

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

Dewey Class. No.: 519.72

Nonlinear algebra in an ACORN = with 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 745733
650 0 $a Mathematical optimization. $3 527675
650 0 $a Matrices. $3 528174
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700 1 $a Tsang, Ken. $3 1240367
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/11022#t=toc