I am interested in the performance of Pyomo to generate an OR model with a huge number of constraints and variables (about 10e6). I am currently using GAMS to launch the optimizations but I would like to use the different python features and therefore use Pyomo to generate the model.
I made some tests and apparently when I write a model, the python methods used to define the constraints are called each time the constraint is instanciated. Before going further in my implementation, I would like to know if there exists a way to create directly a block of constraints based on numpy array data ? From my point of view, constructing constraints by block may be more efficient for large models.
Do you think it is possible to obtain performance comparable to GAMS or other AML languages with pyomo or other python modelling library ?
Thanks in advance for your help !
While you can use NumPy data when creating Pyomo constraints, you cannot currently create blocks of constraints in a single NumPy-style command with Pyomo.  Fow what it's worth, I don't believe that you can in languages like AMPL or GAMS, either.  While Pyomo may eventually support users defining constraints using matrix and vector operations, it is not likely that that interface would avoid generating the individual constraints, as the solver interfaces (e.g., NL, LP, MPS files) are all "flat" representations that explicit represent individual constraints.  This is because Pyomo needs to explicitly generate representations of the algebra (i.e., the expressions) to send out to the solvers.  In contrast, NumPy only has to calculate the result: it gets its efficiency by creating the data in a C/C++ backend (i.e., not in Python), relying on low-level BLAS operations to compute the results efficiently, and only bringing the result back to Python.  
As far as performance and scalability goes, I have generated raw models with over 13e6 variables and 21e6 constraints.  That said, Pyomo was designed for flexibility and extensibility over speed.  Runtimes in Pyomo can be an order of magnitude slower than AMPL when using cPython (although that can shrink to within a factor of 4 or 5 using pypy).  At least historically, AMPL has been faster than GAMS, so the gap between Pyomo and GAMS should be smaller.
Performance of pyomo to generate a model with a huge number of constraints