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.


    • 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.

part I|8 pages

Getting Started

chapter 2Chapter 1|6 pages


part II|34 pages

Probabilistic Modelling

chapter 10Chapter 2|16 pages

Markov Chains

chapter Chapter 3|16 pages

Discrete Event Simulation

part III|26 pages

Dynamical Systems

chapter 44Chapter 4|10 pages

Differential Equations

chapter Chapter 5|14 pages

Systems Dynamics

part IV|26 pages

Emergent Behaviour

chapter 70Chapter 6|10 pages

Game Theory

chapter Chapter 7|14 pages

Agent-Based Simulation

part V|40 pages


chapter 96Chapter 8|18 pages

Linear Programming

chapter Chapter 9|20 pages