Description

Book Synopsis
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.

Table 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.

Introduction to Unconstrained Optimization with R

    Product form

    A Hardback by Shashi Kant Mishra, Bhagwat Ram

    £999.99

    Includes FREE delivery

    Out of stock

      Trusted by thousands of customers. See 2,385+ Customer Reviews

      View other formats and editions of Introduction to Unconstrained Optimization with R by Shashi Kant Mishra

      Book details

      Publisher Springer Verlag, Singapore
      Published 14 January 2020
      ISBN-13 9789811508936
      978-9811508936
      ISBN-10 9811508933
      Also in:
      Mathematics Numerical analysis Optimization

      Description

      Book Synopsis
      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.

      Table 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.

      Recently viewed products

      © 2026 Book Curl

        • American Express
        • Apple Pay
        • Diners Club
        • Discover
        • Google Pay
        • Maestro
        • Mastercard
        • PayPal
        • Shop Pay
        • Union Pay
        • Visa

        Login

        Forgot your password?

        Don't have an account yet?
        Create account