114,29 €
126,99 €
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Introduction to Unconstrained Optimization with R
Introduction to Unconstrained Optimization with R
114,29
126,99 €
  • We will send in 10–14 business days.
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…
126.99
  • Publisher:
  • ISBN-10: 9811508968
  • ISBN-13: 9789811508967
  • Format: 19.6 x 23.4 x 1.3 cm, minkšti viršeliai
  • Language: English
  • SAVE -10% with code: EXTRA

Introduction to Unconstrained Optimization with R (e-book) (used book) | bookbook.eu

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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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  • Author: Shashi Kant Mishra
  • Publisher:
  • ISBN-10: 9811508968
  • ISBN-13: 9789811508967
  • Format: 19.6 x 23.4 x 1.3 cm, minkšti viršeliai
  • Language: English English

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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