Hands-On Mathematical Optimization with Python

In Chapter 5, we claimed that the watershed between easy and difficult problems is their convexity status. Convex optimization problems are, however, a very broad class and one of their downsides is that the dual problem is not always readily available; see the discussion in Section 5.3. In view of the computational benefits of concurrently solving the primal and dual problems, a natural question arises: Is there a subclass of convex optimization problems that are expressive enough to model relevant real-life problems and, at the same time, allow us for a systematic derivation of the dual akin to linear optimization?