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Numerical Methods in Engineering with Python

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This title has free online support material available.

Details

  • 20 tables 364 exercises
  • Page extent: 432 pages
  • Size: 253 x 215 mm
  • Weight: 1.2 kg

Library of Congress

  • Dewey number: 620.001/518
  • Dewey version: 22
  • LC Classification: TA345 .K58 2010
  • LC Subject headings:
    • Engineering mathematics--Data processing
    • Numerical analysis--Data processing
    • Python (Computer program language)

Library of Congress Record

Hardback

 (ISBN-13: 9780521191326)

This text is for engineering students and a reference for practising engineers, especially those who wish to explore Python. This new edition features 18 additional exercises and the addition of rational function interpolation. Brent's method of root finding was replaced by Ridder's method, and the Fletcher-Reeves method of optimization was dropped in favor of the downhill simplex method. Each numerical method is explained in detail, and its shortcomings are pointed out. The examples that follow individual topics fall into two categories: hand computations that illustrate the inner workings of the method and small programs that show how the computer code is utilized in solving a problem. This second edition also includes more robust computer code with each method, which is available on the book website. This code is made simple and easy to understand by avoiding complex bookkeeping schemes, while maintaining the essential features of the method.

• NumArray has been replaced with NumPy, due to the discontinuance of support for NumArray and its predecessor Numeric • Now includes rational function interpolation, Ridder's method and the downhill simplex method • Contains 18 additional exercises

Contents

1. Introduction to Python; 2. Systems of linear algebraic equations; 3. Interpolation and curve fitting; 4. Roots of equations; 5. Numerical differentiation; 6. Numerical integration; 7. Initial value problems; 8. Two-point boundary value problems; 9. Symmetric matrix eigenvalue problems; 10. Introduction to optimization.

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