Linear optimization is a subject that is often neglected in computer science curriculums, even at the graduate level. It is common for computer science students to learn algorithms such as hill climbing or even stochastic local search algorithms such as simulated annealing, but in fact there are many applications for which linear optimization methods can solve the same problems better in a small fraction of the time. If you are a computer scientist or a software engineer with an interest in mathematical optimization, linear and integer programming are must-have tools to round out your knowledge base.<p>A great way to get started is to play around with the solver feature in Excel. Many software engineers may be loath to use this tool, but Excel actually provides a great GUI with which to do simple linear and integer programming problems.
If you are interested in the above text, this textbook might also be of interest: Applied Mathematical Programming by Bradley, Hax, and Magnanti for MIT's Introduction to Optimization. (15.053). Not as code heavy, but still enlightening.<p><a href="http://web.mit.edu/15.053/www/" rel="nofollow">http://web.mit.edu/15.053/www/</a>
I studied Industrial Engineering & Operations Research in undergrad and grad school. Optimization and mathematical modeling has been helpful in consulting, problem-solving, business and programming. I highly recommend taking a deep dive into this subject.