Thursday, July 21, 2011


For a thoughtful comment on the design of Python based modeling system see:

My assessment was clearly not convincing. To summarize: essentially the modeling framework Pyomo is a scalar system. This compares somewhat to the difference between AMPL and GLPK ( Both systems are written in C so we have taken out the language issue (i.e. Python is slow compared to C). The modeling system in GLPK (called Mathprog) is dense (except for the final generated matrix which is of course sparse). This actually work in many cases just fine: many sub-matrices used in the model are often small enough that dense handling is not really a problem. I have seen this in my practice: the majority of the models implemented in GLPK/Mathprog just work fine. Of course there are some models (the big sparse suckers) that bring GLPK/Mathprog on its knees while AMPL will happily generate the model very quickly. If I see this I generally suggest some reformulation or more often just give the advise: buy AMPL.

I suspect this can also happen with Pyomo. For some models a simple dense/scalar structure where we iterate over the carthesian product of indices will not perform. Of course with a system like Pyomo the modeler has more possibilities to implement better data-structures so we can skip zeros quickly and efficiently. But that means we have moved the burden from modeling system to the modeler.


  1. I think I'm still missing your point. Under the hood, the core data structures in Pyomo are sparse. Thus, Pyomo should scale more like AMPL than MathProg, since it's not skipping the zeros. They are not represented.

    So, what am I missing? I don't see how Pyomo moves the burden from the modeling system to the modeler more than AMPL does...

  2. I was afraid of that. I guess I would need to construct some examples to be convincing.