Introduction to Unconstrained Optimization with R

Introduction to Unconstrained Optimization with R
Author :
Publisher : Springer Nature
Total Pages : 309
Release :
ISBN-10 : 9789811508943
ISBN-13 : 9811508941
Rating : 4/5 (941 Downloads)

Book Synopsis Introduction to Unconstrained Optimization with R by : Shashi Kant Mishra

Download or read book Introduction to Unconstrained Optimization with R written by Shashi Kant Mishra and published by Springer Nature. This book was released on 2019-12-17 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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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