Optimization Modelling Using R

This book covers the use of R for optimization, a key area of operations research, with a focus on linear and mixed integer optimization. It uses an algebraic modeling approach and emphasizes creating formulations before implementation. It uses open-source tools and promotes reproducible research. The book is suitable for graduate and undergraduate courses and can be used by students with varying mathematical backgrounds.

Format: Hardback
Length: 274 pages
Publication date: 05 July 2022
Publisher: Taylor & Francis Ltd

This comprehensive guide delves into the realm of optimization, a fundamental aspect of operations research, which has found widespread application across diverse industries. The focus lies specifically on linear and mixed integer optimization, employing an algebraic modeling approach to develop formulations that seamlessly align with an algebraic implementation in the R programming language. In the current era of data analytics, where the extraction, analysis, and interpretation of data hold immense importance, a robust data analytics platform emerges as a crucial tool. Both working technology and business professionals require a solid understanding of the tools and language of data analysis. R plays a pivotal role in lowering the barriers to entry for individuals seeking to embark on their data analytics journey, enabling them to harness the power of data-driven decision-making.

Philosophically, this book emphasizes the importance of formulating ideas before delving into their implementation. The utilization of algebraic representation enables clear comprehension and generalization of complex applications, while also serving as a means to communicate and explain the modeling decisions made. To enhance the reader's understanding, Appendix A provides a comprehensive introduction to R, encompassing mathematics at the level of subscripts and summations. Additionally, Appendix B offers refreshers to reinforce key concepts.

This book stands out for several reasons. Firstly, it offers concise and self-contained code examples, making them relatively easy to understand and follow. Secondly, it prioritizes the creation of algebraic formulations before implementing solutions, emphasizing the application-oriented nature of optimization modeling. Thirdly, the book focuses on providing practical insights rather than delving into intricate algorithmic details, making it accessible to readers with varying mathematical backgrounds.

Furthermore, this book aligns with the principles of reproducible research, ensuring that all materials are readily available on the authors' GitHub repository. This open-source approach fosters collaboration, enables further development, and promotes the dissemination of optimization modeling techniques.

In addition to its theoretical aspects, the book demonstrates common debugging practices with a troubleshooting emphasis tailored specifically to optimization modeling using R. By providing code readers with the ability to adapt and customize it to their own applications, this book serves as a valuable resource for both students and professionals in the field.

In conclusion, this book is a comprehensive and practical guide to optimization modeling using R. It offers a solid foundation in algebraic modeling, emphasizes the importance of formulation, and provides readers with the tools and knowledge necessary to excel in the field of data analytics. Whether you are a student seeking to expand your optimization expertise or a professional looking to enhance your data analysis capabilities, this book is an invaluable resource.

Weight: 562g
Dimension: 162 x 241 x 24 (mm)
ISBN-13: 9780367507893