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Introduction to computational models with Python /
Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press,
[2016]
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| Colección: | Chapman & Hall/CRC computational science series.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Garrido, José M., |e author. | |
| 245 | 1 | 0 | |a Introduction to computational models with Python / |c José M. Garrido, Kennesaw State University, Kennesaw, Georgia, USA. |
| 264 | 1 | |a Boca Raton, FL : |b CRC Press, |c [2016] | |
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy modules. Divided ed into five sections, the book first introduces the basic models and techniques for designing and implementing problem solutions, independent of software and hardware tools. The second section discusses programming principles with the Python programming language, covering basic programming concepts, data definitions, programming structures with flowcharts and pseudo-code, solving problems, and algorithms. The third section describes Python lists, arrays, basic data structures, object orientation, linked lists and recursion, and running programs under Linux. In the fourth section, examples and case studies demonstrate the application of programming principles and fundamental techniques to relatively simple computational models. The final section focuses on the modeling of linear optimization problems, from problem formulation to implementation of computational models. | ||
| 505 | 0 | |a Section 1. Problem solving -- section 2. Basic programming principles with Python -- section 3. Data structures, object orientation, and recursion -- section 4. Fundamental computational models with Python -- section 5. Linear optimization models. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Mathematical models |x Data processing. | |
| 650 | 0 | |a Python (Computer program language) | |
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| 650 | 6 | |a Python (Langage de programmation) | |
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| 650 | 7 | |a Mathematical models |x Data processing |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
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| 776 | 0 | 8 | |i Print version: |a Garrido, José M. |t Introduction to computational models with Python. |d Boca Raton, FL : CRC Press, [2016] |z 9781498712033 |z 1498712037 |
| 830 | 0 | |a Chapman & Hall/CRC computational science series. | |
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