Author of said MATLAB package (CVX) here. Yours is a really interesting observation!
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.
I agree with all your points. CVX made it so that students like me spent more time learning the material and techniques and less time worrying about implementation. Great work!
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.