Introduction to Computational Modeling Using C and Open-Source Tools

Introduction to Computational Modeling Using C and Open-Source Tools
Author :
Publisher : CRC Press
Total Pages : 463
Release :
ISBN-10 : 9781482216783
ISBN-13 : 1482216787
Rating : 4/5 (787 Downloads)

Book Synopsis Introduction to Computational Modeling Using C and Open-Source Tools by : Jose M. Garrido

Download or read book Introduction to Computational Modeling Using C and Open-Source Tools written by Jose M. Garrido and published by CRC Press. This book was released on 2013-11-13 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 science.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.


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