Introduction to Unconstrained Optimization with R
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22 690 Ft (21 609 Ft + 5% VAT)
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22 690 Ft
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
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Product details:
- Edition number 1st ed. 2019
- Publisher Springer
- Date of Publication 15 January 2021
- Number of Volumes 1 pieces, Book
- ISBN 9789811508967
- Binding Paperback
- No. of pages304 pages
- Size 235x155 mm
- Weight 583 g
- Language English
- Illustrations 715 Illustrations, black & white; 50 Illustrations, color 171
Categories
Short description:
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.
MoreLong description:
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.
MoreTable of Contents:
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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