[z4chj]

This looks like a great piece of hard work covering topics that are not easy to grasp. I am wondering why the author(s) chose to write their code samples in Julia. Most algorithm textbooks I have come across use their own "pseudocode" which often ends up being challenging to read, but in a perfect world, I would selfishly like to have all of the code snippets in Python. Even though this textbook doesn't do that (although let's not rule out future optimizations!), the amount of work that went into this was clearly astounding and well done.

[chrispeel]

Mykel Kochenderfer is a coauthor on the book and the PhD advisor to Tim. The video [1] below describes how Kochenderfer came to be using Julia (it's faster than Python, and has an AST that the formal verification guys like).

[1] http://www.youtube.com/watch?v=rj-WhTL_VXE&t=16m9s

[Mageek]

Many of the algorithms would be very laborious to implement and explain in a language like C or C++. Julia has the advantage that the actual code looks very much like the pseudocode you mention. In fact, the guideline implementation for the ACAS X collision avoidance software that my prof developed, that is to become the de-facto issued by the FAA, has been produced in Julia rather than in pseudocode because it actually runs. Real code has no ambiguity. (assuming you avoid undefined behavior). Julia is a fantastic language. Really hope it catches on.

[bachmeier]

I really wish that all of these books had a wiki so that users could post their own solutions with corresponding discussion. Particularly practical implementation details that don't belong in the text of the book.