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

Length: 142 pages
Edition: 1
Language: English
Publisher: Chapman & Hall
Publication Date: 2022-05-12
ISBN-10: 0367348683
ISBN-13: 9780367348687
Sales Rank: #0 (See Top 100 Books)

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.

Cover Page
Half-Title Page
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