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Numerical Methods in Engineering with Python

✍ Scribed by Jaan Kiusalaas


Publisher
Cambridge University Press
Year
2010
Tongue
English
Leaves
434
Category
Library

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✦ Synopsis


Über das Produkt

Numerical Methods in Engineering with Python, 2nd Edition is intended for engineering students and as a reference for practising engineers interested in exploring Python. This new edition features 18 more exercises, more robust computer codes, and the addition of rational function interpolation, Ridder's method, and the downhill simplex method.

Kurzbeschreibung

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.

✦ Subjects


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Numerical Methods in Engineering with Py
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Being familiar with python is necessary for this volume, but the concepts used are simple. The description of the numerical methods is complete, but a comparison of methods is not always present leaving an inexperienced analyst to wonder which method is the best for a given situation. Python is al

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Numerical Methods in Engineering with Python is a text for engineering students and a reference for practicing engineers, especially those who wish to explore the power and efficiency of Python. Examples and applications were chosen for their relevance to real world problems, and where numerical sol

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