An Introduction to Optimization: With Applications in Machine Learning and Data Analytics

The primary goal of this text is a practical one. Equipping students with the enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop marketable skill set for mathematics majors but also for students of engineering, computer science, economics, statistics, and business. Optimization reaches into many different fields. This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course. The book contains many applications but also explains the mathematics behind the techniques. There are even definitions and theorems. Optimization techniques are at the heart of the first spam filters, are used in self-driving cars, play a great role in machine learning and can be used in such places as determining a batting order in a Major League Baseball game. Additionally, it has seemingly limitless other applications in business and industry. In short, knowledge of this subject offers an individual both a very marketable skill set for a wealth of jobs as well as useful tools for research in many academic disciplines. Many of the problems rely on using a computer. Microsoft's Excel is most often used as this is common in business, but Python and other languages are considered. The reason for this is to experience mathematics and engineering students using MatLab or Mathematica, the economics and business majors using Excel, and the computer science students writing their own programs in Java or Python.