This book covers using R for doing optimization, a key area of operations research, which has been applied to virtually every industry. The focus is on linear and mixed integer optimization. It uses an algebraic modeling approach for creating formulations that pairs naturally with an algebraic implementation in R.
With the rapid rise of interest in data analytics, a data analytics platform is key. Working technology and business professionals need an awareness of the tools and language of data analysis. R reduces the barrier to entry for people to start using data analytics tools.
Philosophically, the book emphasizes creating formulations before going into
implementation. Algebraic representation allows for clear understanding and generalization
•f large applications, and writing formulations is necessary to explain and convey the modeling decisions made.
Appendix A introduces R. Mathematics is used at the level of subscripts and summations Refreshers are provided in Appendix B.
This book:
• Provides and explains code so examples are relatively clear and self-contained.
• Emphasizes creating algebraic formulations before implementing.
• Focuses on application rather than algorithmic details.
• Embodies the philosophy of reproducible research.
• Uses open-source tools to ensure access to powerful optimization tools.
• Promotes open-source: all materials are available on the author’s github repository.
• Demonstrates common debugging practices with a troubleshooting emphasis specific to optimization modeling using R.
• Provides code readers can adapt to their own applications
.
This book can be used for graduate and undergraduate courses for students without a background in optimization and with varying mathematical backgrounds.