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by vrc
1110 days ago
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I had the pleasure of taking this course with Prof. Boyd when he did a semester at MIT and it really was excellent. With a basic understanding of linear algebra and proofs it opened my eyes to so many techniques and ways to look at problems. It also lowered my fear of tackling more complex coursework because it motivated my interest. The only downside is that I became far too over reliant on the MATLAB package they made to pair with the course, so trying to implement some of the techniques later on from scratch took some doing. |
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When the book was created, CVX didn't exist. Instead, Boyd & Vandenberghe wrote separate MATLAB scripts for virtually every figure (to be fair, with a lot of cut-and-paste and convenience functions). The course did hum along pretty well without CVX (or its later and now better-supported Python equivalent CVXPY), for sure.
I think it is fair to say that these software packages made convex optimization far more accessible a topic. Certainly, implementing some of the solution techniques is not straightforward. But far more people can actually spend time using convex optimization in their application domains if they don't have to concern themselves with those implementation complexities.
Incidentally, on the commercial side, Mosek ApS and Gurobi both offer Python-based modeling frameworks for convex optimization that do a great job of making the discipline accessible as well. They don't operate in quite the same way as CVX and CVXPY, but that's not really important: what matters is that people can readily solve their problems, not the specific approach that gets that done.