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Introduction to Unconstrained Optimization with R

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Introduction to Unconstrained Optimization with R/ by Shashi Kant Mishra, Bhagwat Ram.
Author:	Mishra, Shashi Kant.
other author:	Ram, Bhagwat.
Description:	XVI, 304 p. 765 illus., 50 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Mathematical 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. - 1st ed. 2019. - XVI, 304 p. 765 illus., 50 illus. in color.online resource.

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:

527675
Mathematical optimization.

LC Class. No.: QA402.5-402.6

Dewey Class. No.: 519.6

Introduction to Unconstrained Optimization with R
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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.
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