Using GAMS/CPLEX from Python PYOMO

问题

I noticed that Pyomo 5.3 offers a GAMS solver plugin. https://github.com/Pyomo/pyomo/blob/master/pyomo/solvers/plugins/solvers/GAMS.py

This is very exciting, as we have a GAMS/CPLEX license where we can use CPLEX as solver, but only via GAMS. With the new Pyomo-Gams interface, it should from my understanding be possible to formulate a problem in Pyomo, and have it translated to GAMS and solved by CPLEX.

However, when I test this with the shell integration, it is very slow (40s for 30 solves of a small MIP versus 6s with glpk/ipopt/cbc). Also, the documentation of the plugin is effectively non-existent.

But maybe someone of you has some experience using that interface and can help me with it

does pyomo actually translate the pyomo model into gams code? If yes, where can I find the gams-file?
how efficient is the translation, and how should I proceed if I want to solve a small model repeatedly?
what is the difference between using the shell or the GAMS Python API?
is there any place to find documentation about this?
Also, it seems that conda provides Pyomo 5.3 only for Linux/Python 3.6 OR for Windows/Python 2.7 https://anaconda.org/conda-forge/pyomo/files?version=5.3, so I had to use pip to install Pyomo 5.3 on my machine.

Thanks in advance, Theo

import pyomo.environ as pe

# set up the model
model = pe.ConcreteModel()

model.MaxWeight = pe.Param(initialize=0,mutable=True)
model.Item = ['hammer','wrench','screwdriver','towel']

Weight = {'hammer':5,'wrench':7,'screwdriver':4,'towel':3}
Value = {'hammer':8,'wrench':3,'screwdriver':6,'towel':11}

model.x = pe.Var(model.Item,within=pe.Binary)
model.z = pe.Objective(expr=sum(Value[i] * model.x[i] for i in model.Item),sense=pe.maximize)
model.constraint = pe.Constraint(expr=sum(Weight[i]*model.x[i] for i in model.Item) <= model.MaxWeight)

# time execution
solver_list = ['cbc', 'ipopt', 'gams', 'glpk']

for i, solver_name in enumerate(solver_list):
    solver = pe.SolverFactory(solver_name)
    print(solver_name)
    tic = time.time()
    for MaxWeight_i in range(0,30):
        model.MaxWeight = MaxWeight_i
        result = solver.solve(model)

        soln_items = list()
        for i in model.x:
            if pe.value(model.x[i]) > 0.5:
                soln_items.append(i)
        # print("Maximum Weight =", MaxWeight_i, soln_items)

    print("{:7.2f} s".format(time.time()-tic))
    print(" ")

回答1:

This is rather delayed, but I can answer a few of your questions.

First, a basic documentation page was just created for the GAMS interface on readthedocs which you can find at: http://pyomo.readthedocs.io/en/latest/library_reference/solvers/gams.html. Note that this location may change as I believe we are restructuring the documentation tree some time soon, but you should be able to search for "gams" to find it again in the future. If there's more documentation that you believe you or others would like to see, please let me know as I'd be happy to provide anything that would be helpful.

As for the difference between the shell interface and the Python API interface, there really isn't any. I thought there would have been a performance increase by using the API but that didn't seem to be the case in the past (and in fact the one model I tried it on saw the shell interface be faster anyway). If you try both and experience otherwise, again I'd be happy to know that too.

来源：https://stackoverflow.com/questions/48222658/using-gams-cplex-from-python-pyomo

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pyomo

gams-math