Applied Mathematics with Open-Source Software: Operational Research Problems with Python and R

✍ Scribed by Vincent Knight, Geraint Palmer

• Publisher: Chapman and Hall/CRC
• Year: 2022
• Tongue: English
• Leaves: 153
• Series: Chapman & Hall/CRC Series in Operations Research
• Edition: 1
• Category: Library

✦ Synopsis

Applied Mathematics with Open-source Software: Operational Research Problems with Python and R is aimed at a broad segment of readers who wish to learn how to use open-source software to solve problems in applied mathematics. The book has an innovative structure with 4 sections of two chapters covering a large range of applied mathematical techniques: probabilistic modelling, dynamical systems, emergent behaviour and optimisation. The pairs of chapters in each section demonstrate different families of solution approaches. Each chapter starts with a problem, gives an overview of the relevant theory, shows a solution approach in R and in Python, and finally gives wider context by including a number of published references. This structure will allow for maximum accessibility, with minimal prerequisites in mathematics or programming as well as giving the right opportunities for a reader wanting to delve deeper into a particular topic.

Features

• An excellent resource for scholars of applied mathematics and operational research, and indeed any academics who want to learn how to use open-source software.

• Offers more general and accessible treatment of the subject than other texts, both in terms of programming language but also in terms of the subjects considered.

• The R and Python sections purposefully mirror each other so that a reader can read only the section that interests them.

• An accompanying open-source repository with source files and further examples is posted online at https://bit.ly/3kpoKSd.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Authors
SECTION I: Getting Started
CHAPTER 1: Introduction
1.1. WHO IS THIS BOOK FOR?
1.2. WHAT DO WE MEAN BY APPLIED MATHEMATICS?
1.3. WHAT IS OPEN-SOURCE SOFTWARE
1.4. HOW TO GET THE MOST OUT OF THIS BOOK
1.5. HOW CODE IS WRITTEN IN THIS BOOK
SECTION II: Probabilistic Modelling
CHAPTER 2: Markov Chains
2.1. PROBLEM
2.2. THEORY
2.3. SOLVING WITH PYTHON
2.4. SOLVING WITH R
2.5. WIDER CONTEXT
CHAPTER 3: Discrete Event Simulation
3.1. PROBLEM
3.2. THEORY
3.2.1. Event Scheduling Approach
3.2.2. Process-Based Simulation
3.3. SOLVING WITH PYTHON
3.4. SOLVING WITH R
3.5. WIDER CONTEXT
SECTION III: Dynamical Systems
CHAPTER 4: Differential Equations
4.1. PROBLEM
4.2. THEORY
4.3. SOLVING WITH PYTHON
4.4. SOLVING WITH R
4.5. WIDER CONTEXT
CHAPTER 5: Systems Dynamics
5.1. PROBLEM
5.2. THEORY
5.3. SOLVING WITH PYTHON
5.4. SOLVING WITH R
5.5. WIDER CONTEXT
SECTION IV: Emergent Behaviour
CHAPTER 6: Game Theory
6.1. PROBLEM
6.2. THEORY
6.3. SOLVING WITH PYTHON
6.4. SOLVING WITH R
6.5. WIDER CONTEXT
CHAPTER 7: Agent-Based Simulation
7.1. PROBLEM
7.2. THEORY
7.3. SOLVING WITH PYTHON
7.4. SOLVING WITH R
7.5. WIDER CONTEXT
SECTION V: Optimisation
CHAPTER 8: Linear Programming
8.1. PROBLEM
8.2. THEORY
8.3. SOLVING WITH PYTHON
8.4. SOLVING WITH R
8.5. WIDER CONTEXT
CHAPTER 9: Heuristics
9.1. PROBLEM
9.2. THEORY
9.3. SOLVING WITH PYTHON
9.4. SOLVING WITH R
9.5. WIDER CONTEXT
Bibliography
Index