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2011d.00824 Kiusalaas, Jaan Numerical methods in engineering with Python. 2nd ed. Cambridge: Cambridge University Press (ISBN 978-0-521-19132-6/pbk). x, 422~p. (2010). 2010 Cambridge: Cambridge University Press EN N15 R25 Python rational function interpolation Brent's method root finding Ridder's method Fletcher-Reeves method computer code NumArray NumPy systems of linear algebraic equations curve fitting numerical differentiation
• doi:10.2277/0511126751
• From the preface: 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 Web site. This code is made simple and easy to understand by avoiding complex bookkeeping schemes, while maintaining the essential features of the method. The major change in the second edition is the replacement of NumArray (a Python extension that implements array objects) with NumPy. The reason for the changeover is the imminent discontinuance of support for NumArray and its predecessor Numeric; Rational function interpolation was added to Chapter 3; Brent's method of root finding in Chapter 4 was replaced by Ridder's method; The Fletcher-Reeves method of optimization was drooped in favor of the downhill simplex method in Chapter 10. 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.