JLee_LinearOptimizationBook VS Probabilistic-Programmin

I started with Lee, J. “A first course in linear optimization”. While the description on linear programming may be slightly different from the standard algorithm used in practice, it provided me a good insight on why it works in a theoretical sense. Even better, it’s available for free at https://github.com/jon77lee/JLee_LinearOptimizationBook/blob/master/JLee.4.01.pdf

Here's an excerpt of a comment I previously made on Hacker News:

I'm a Ph.D. student in operations research (OR). My suggestion would be to first build a strong foundation in linear programming. This will introduce you to the notion of duality, which is heavily emphasized in many mathematical programming courses. Here's a good open-source book on linear programming written by Jon Lee, the current editor of Mathematical Programming A: https://github.com/jon77lee/JLee_LinearOptimizationBook

Then I'd suggest studying more general methods for continuous and convex optimization. The book I see mentioned a lot is Convex Optimization by Boyd and Vandenberghe, although we didn't use this in our coursework. Instead, we used a lot of the material presented here: http://mitmgmtfaculty.mit.edu/rfreund/educationalactivities/

If you read the above (or any other two books on linear programming and convex optimization), you'll probably have a better idea of what you want to study next and how you want to go about it. The next natural step would be to study combinatorial (i.e., integer or mixed-integer) optimization. (Jon Lee has another book on this subject; I've also heard good things about the Schrijver book.)

I recommend Bayesian Methods for hackers [1]. It doesn’t go too deep on theory but I feel like it’s well written and has really good coded up examples of theory being applied to problems. It has a relatively narrow scope, but I find myself reaching for methods I learned from the author frequently.

[1] https://github.com/CamDavidsonPilon/Probabilistic-Programmin...