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Introduction to unconstrained optimi...

Mishra, Shashi Kant.

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Introduction to unconstrained optimization with R

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Introduction to unconstrained optimization with R/ by Shashi Kant Mishra, Bhagwat Ram.
作者:	Mishra, Shashi Kant.
其他作者:	Ram, Bhagwat.
出版者:	Singapore :Springer Singapore : : 2019.,
面頁冊數:	xvi, 304 p. :ill., digital ;24 cm.
內容註:	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
標題:	Constrained optimization. -
電子資源:	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

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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.
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