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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
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
Section I. Getting Started. 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. 2. Markov Chains. 2.1. Problem. 2.2. Theory. 2.3. Solving with Python. 2.4. Solving with R. 2.5. Wider Context. 3.Discrete Event Simulation. 3.1. Problem. 3.2. Theory. 3.3. Solving with Python. 3.4. Solving with R. 3.5. Wider Context. Section III. Dynamical Systems. 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. 6. Game Theory. 6.1. Problem. 6.2. Theory. 6.3. Solving with Python. 6.4. Solving with R. 6.5. Wider Context. 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. 8. Linear Programming. 8.1. Problem. 8.2. Theory. 8.3. Solving with Python. 8.4. Solving with R. 8.5. Wider Context. 9. Heuristics. 9.1. Problem. 9.2. Theory. 9.3. Solving with Python. 9.4. Solving with R. 9.5. Wider Context.
Vince Knight is a Senior Lecturer at Cardiff
University in the School of Mathematics. His research interests are
in emergent behaviour, probabilistic modelling, applications in
healthcare and pedagogy. He maintains a number of open-source
research software projects, is a trustee of the UK Python
association, is an editor for the Journal of Open-Source Software,
was awarded the 2017 John Pinner award for contribution to the
Python community and is a fellow of the Sustainable Software
Institute. He regularly wins awards for his teaching in the School
of Mathematics. He does not only speak at conferences around the
world but continues to organise conferences to bring the power of
open-source software to as many people as possible.
Geraint Palmer is a Welsh Medium Lecturer at Cardiff
University in the School of Mathematics. He is a member of the
operational research group where his research interests are in
simulation and probabilistic modelling, in particular applying
these to model public services such as healthcare systems. He uses
open-source software in all aspects of his research: he is a
maintainer of Ciw, an open-source Python library for discrete event
simulation, and won the OR Society's Doctoral Award in 2018.
Geraint is also a fellow of the Software Sustainability Institute
and has presented at a number of international conferences on the
subject of best practice of scientific computing, and regularly
teaches programming and runs coding workshops for people of all
ages.
"This is an outstanding introduction to Operational Research,
providing a hands-on, practical discussion of a number of key
topics, with examples, exercises and an excellent use of
open-source software that allows the learner to explore the topics
and ideas for themselves. Also, each chapter ends with a discussion
of the wider context, including details of more advanced work and
different applications, together with appropriate references to
published work in the literature. In the past I have taught similar
course to Maths, Computer Science and Business students, using
software where available. However, this book allows the student to
go much further, taking control of their own learning and adapting
the code to explore the different topics more fully. As such, I
would strongly encourage anyone teaching or learning Operational
Research to buy and use this excellent resource."
– Sally McClean FRSS, Ulster University, United Kingdom"What an
exciting contribution to Open Science and mathematics from Knight
and Palmer. Essential reading for all Operational Researchers who
wish to use Free and Open-Source Software and share their models
with others."
– Thomas Monks, University of Exeter, United Kingdom"This book is
targeted towards a wide range of readers including students with
interest in applied mathematics, operational research and related
disciplines, programmers, or just hobbyist who like to solve maths
problems. This book covers problems on various topics including
Markov Chains, Discrete Event simulation, Differential Equations,
Game Theory, Systems Dynamics, Agent-based simulation, Linear
Programming, and Heuristics. There is something for everybody’s
taste! The programming codes are available in both Python and
open-source software package R and the codes are easy to follow and
understand."
– Sanja Petrovic, Nottingham University Business School, United
Kingdom
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