[wenc]

This is a book about mathematical optimization, not code optimization. It has its place in the world (kinda like an engineering handbook), but the field is constantly evolving and actively researched (especially in frontiers like global optimization and nonlinear nonconvex optimization; linear problems are more mature but still moving at a clip, as witnessed by the vast improvement in solvers over the years).

The danger of indexing too much on a canon of knowledge in a fast evolving field is that you're narrowing your view to a set of techniques that don't work so well on modern problems.

Deep learning for instance is a nonconvex optimization problem where we have a lot of practical knowledge on how to make it work well, but the theoretical knowledge of why it works so well is still being developed. This is a case where practice precedes theory.

Instead of an encyclopedia, I recommend subscribing to a (free) mailing list of pre-prints, Optimization Online.

https://optimization-online.org/