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Introduction to unconstrained optimi...

Mishra, Shashi Kant.

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Introduction to unconstrained optimization with R

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to unconstrained optimization with R/ by Shashi Kant Mishra, Bhagwat Ram.
Author:	Mishra, Shashi Kant.
other author:	Ram, Bhagwat.
Published:	Singapore :Springer Singapore : : 2019.,
Description:	xvi, 304 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. Mathematical Foundations -- 3. Basics of R -- 4. First Order and Second Order Necessary Conditions -- 5. One Dimensional Optimization Methods -- 6. Steepest Descent Method -- 7. Newton's Method -- 8. Conjugate Direction Methods -- 9. Quasi-Newton Methods.
Contained By:	Springer eBooks
Subject:	Constrained optimization. -
Online resource:	https://doi.org/10.1007/978-981-15-0894-3
ISBN:	9789811508943

Introduction to unconstrained optimization with R
Mishra, Shashi Kant.

Introduction to unconstrained optimization with R[electronic resource] /by Shashi Kant Mishra, Bhagwat Ram. - Singapore :Springer Singapore :2019. - xvi, 304 p. :ill., digital ;24 cm.

1. Introduction -- 2. Mathematical Foundations -- 3. Basics of R -- 4. First Order and Second Order Necessary Conditions -- 5. One Dimensional Optimization Methods -- 6. Steepest Descent Method -- 7. Newton's Method -- 8. Conjugate Direction Methods -- 9. Quasi-Newton Methods.

This book discusses unconstrained optimization with R - a free, open-source computing environment, which works on several platforms, including Windows, Linux, and macOS. The book highlights methods such as the steepest descent method, Newton method, conjugate direction method, conjugate gradient methods, quasi-Newton methods, rank one correction formula, DFP method, BFGS method and their algorithms, convergence analysis, and proofs. Each method is accompanied by worked examples and R scripts. To help readers apply these methods in real-world situations, the book features a set of exercises at the end of each chapter. Primarily intended for graduate students of applied mathematics, operations research and statistics, it is also useful for students of mathematics, engineering, management, economics, and agriculture.

ISBN: 9789811508943

Standard No.: 10.1007/978-981-15-0894-3doiSubjects--Topical Terms:

1066669
Constrained optimization.

LC Class. No.: QA323 / .M57 2019

Dewey Class. No.: 519.6

Introduction to unconstrained optimization with R
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100 1 $a Mishra, Shashi Kant. $3 899068
245 1 0 $a Introduction to unconstrained optimization with R $h [electronic resource] / $c by Shashi Kant Mishra, Bhagwat Ram.
260 $a Singapore : $c 2019. $b Springer Singapore : $b Imprint: Springer,
300 $a xvi, 304 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1. Introduction -- 2. Mathematical Foundations -- 3. Basics of R -- 4. First Order and Second Order Necessary Conditions -- 5. One Dimensional Optimization Methods -- 6. Steepest Descent Method -- 7. Newton's Method -- 8. Conjugate Direction Methods -- 9. Quasi-Newton Methods.
520 $a This book discusses unconstrained optimization with R - a free, open-source computing environment, which works on several platforms, including Windows, Linux, and macOS. The book highlights methods such as the steepest descent method, Newton method, conjugate direction method, conjugate gradient methods, quasi-Newton methods, rank one correction formula, DFP method, BFGS method and their algorithms, convergence analysis, and proofs. Each method is accompanied by worked examples and R scripts. To help readers apply these methods in real-world situations, the book features a set of exercises at the end of each chapter. Primarily intended for graduate students of applied mathematics, operations research and statistics, it is also useful for students of mathematics, engineering, management, economics, and agriculture.
650 0 $a Constrained optimization. $3 1066669
650 0 $a R (Computer program language) $3 784593
650 1 4 $a Optimization. $3 891104
650 2 4 $a Computational Mathematics and Numerical Analysis. $3 891040
700 1 $a Ram, Bhagwat. $3 3454474
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-981-15-0894-3
950 $a Mathematics and Statistics (Springer-11649)