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Emphasizing analytical skill development and problem solving, this book shows how to implement computational models using the flexible and easy-to-use Python programming language. It provides the foundation for more advanced work in scientific computing. 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. The Python source code and data files are available on the author's website.
List of contents
Problem Solving: Problem Solving and Computing. Simple Python Programs. Basic Programming Principles with Python: Modules and Functions. Program Structures. The Selection Program Structure. The Repetition Program Structure. Data Structures, Object Orientation, and Recursion: Python Lists, Strings, and Other Data Sequences. Object Orientation. Object-Oriented Programs. Linked Lists. Recursion. Fundamental Computational Models with Python: Computational Models with Arithmetic Growth. Computational Models with Quadratic Growth. Models with Geometric Growth. Computational Models with Polynomial Growth. Empirical Models with Interpolation and Curve Fitting. Using Arrays with Numpy. Models with Matrices and Linear Equations. Introduction to Models of Dynamical Systems. Linear Optimization Models: Linear Optimization Modeling. Solving Linear Optimization Models. Sensitivity Analysis and Duality. Transportation Models. Network Models. Integer Linear Optimization Models.
About the author
José M. Garrido is a professor in the Department of Computer Science at Kennesaw State University. Dr. Garrido is the author of several books and numerous research papers. His research interests include software development, operating systems, computational modeling, object-oriented simulation, and system formal specification.
