The algorithms listed were intentionally chosen to be easy to solve.

Calculating general equilibrium over non divisible goods is NP hard. It is practically infeasible because your problem size is eight billion people each choosing from hundreds of millions of products to produce or consume.

Another problem is basically any form of non convex optimization because even the approximations require describing a non convex polygon as piecewise linear segments and therefore even the approximation algorithm are NP hard.

Now you will probably be like "what's the big deal? Just solve it like any other NP hard problem, with brute force. You only need to solve it once to prove that it is solvable."

Unfortunately this theoretical ability to solve a problem is useless in practice, because you need to solve the problem frequently. Let's say a thousand times per second. Yes, you only have a millisecond to solve the problem and you must produce an answer within that deadline.

In practice everyone has given up and uses QP approximations instead, disproving the premise of the article. You are better off with memorization based systems that classify the situation and then choose a memorized answer, like neural networks, and only after that do you actually try to use the QP solver to refine the solution. So yeah, if you build a machine like that you're throwing your hands up a thousand times per second saying "can't be done".