> Unless you're solving toy problems, it matters quite a bit. In mathematical optimization, constructing the problem tends to be just as expensive as solving the problem is – sometimes more so.

With all due respect, this is just false. I can construct a million dimensional linear program for a large network problem in a few seconds in python. Solving it with cplex can take minutes.

I have a Ph.D. in computational mathematics and do this for a living. I write lots of C, and still almost exclusively use Python or Lua for problem formulation because the performance just doesn't matter.

I'm certain there are some cases where it's true. One I've encountered is doing real-time solution of MILP for adaptive path planning. Here the problems are of moderate size and you want to reconstruct them from geometry data every 10ms or so. Here Python will indeed bite you in the problem formulation stage.