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 numerical computing, such as the Numpy and Scipy modules. The Python source code and data files are available on the author’s website.

The book’s five sections present:

1. An overview of problem solving and simple Python programs, introducing the basic models and techniques for designing and implementing problem solutions, independent of software and hardware tools
2. Programming principles with the Python programming language, covering basic programming concepts, data definitions, programming structures with flowcharts and pseudo-code, solving problems, and algorithms
3. Python lists, arrays, basic data structures, object orientation, linked lists, recursion, and running programs under Linux
4. Implementation of computational models with Python using Numpy, with examples and case studies
5. The modeling of linear optimization problems, from problem formulation to implementation of computational models

This book introduces the principles of computational modeling as well as the approaches of multi- and interdisciplinary computing to beginners in the field. It provides the foundation for more advanced studies in scientific computing, including parallel computing using MPI, grid computing, and other methods and techniques used in high-performance computing.

Table of Contents

Section 1 Problem Solving
Chapter 1 Problem Solving And Computing
Chapter 2 Simple Python Programs

Section 2 Basic Programming Principles With Python
Chapter 3 Modules And Functions
Chapter 4 Program Structures
Chapter 5 The Selection Program Structure
Chapter 6 The Repetition Program Structure

Section 3 Data Structures, Object Orientation, And Recursion
Chapter 7 Python Lists, Strings, And Other Data Sequences
Chapter 8 Object Orientation
Chapter 9 Object- Oriented Programs
Chapter 10 Linked Lists
Chapter 11 Recursion

Section 4 Fundamental Computational Models With Python
Chapter 12 Computational Models With Arithmetic Growth
Chapter 13 Computational Models With Quadratic Growth
Chapter 14 Models With Geometric Growth
Chapter 15 Computational Models With Polynomial Growth
Chapter 16 Empirical Models With Interpolation And Curve Fitting
Chapter 17 Using Arrays With Numpy
Chapter 18 Models With Matrices And Linear Equations
Chapter 19 Introduction To Models Of Dynamical Systems

Section 5 Linear Optimization Models
Chapter 20 Linear Optimization Modeling
Chapter 21 Solving Linear Optimization Models
Chapter 22 Sensitivity Analysis And Duality
Chapter 23 Transportation Models
Chapter 24 Network Models
Chapter 25 Integer Linear Optimization Models