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Introduction to Computational Modeling Using C and Open-Source Tools

List Price: $87.99
SKU:
9780367379230
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  • Product Details

    Author:
    Jose M. Garrido
    Format:
    Paperback
    Pages:
    464
    Publisher:
    CRC Press (June 19, 2019)
    Language:
    English
    ISBN-13:
    9780367379230
    Weight:
    23.25oz
    Dimensions:
    6.125" x 9.1875"
    File:
    TAYLORFRANCIS-TayFran_260403050835162-20260403.xml
    Folder:
    TAYLORFRANCIS
    List Price:
    $87.99
    Country of Origin:
    United States
    Case Pack:
    10
    As low as:
    $83.59
    Publisher Identifier:
    P-CRC
    Discount Code:
    H
    Audience:
    Professional and scholarly
    Pub Discount:
    30
    Imprint:
    Chapman and Hall/CRC
  • Overview

    Introduction to Computational Modeling Using C and Open-Source Tools presents the fundamental principles of computational models from a computer science perspective. It explains how to implement these models using the C programming language. The software tools used in the book include the Gnu Scientific Library (GSL), which is a free software library of C functions, and the versatile, open-source GnuPlot for visualizing the data. All source files, shell scripts, and additional notes are located at ksuweb.kennesaw.edu/~jgarrido/comp_models.



    The book first presents an overview of problem solving and the introductory concepts, principles, and development of computational models before covering the programming principles of the C programming language. The author then applies programming principles and basic numerical techniques, such as polynomial evaluation, regression, and other numerical methods, to implement computational models. He also discusses more advanced concepts needed for modeling dynamical systems and explains how to generate numerical solutions. The book concludes with the modeling of linear optimization problems.





    Emphasizing analytical skill development and problem solving, this book helps you understand how to reason about and conceptualize the problems, generate mathematical formulations, and computationally visualize and solve the problems. It provides you with the foundation to understand more advanced scientific computing, including parallel computing using MPI, grid computing, and other techniques in high-performance computing.