model.obj = Objective(expr=(((model.p - model.x)**2) +
((model.q - model.y)**2))**0.5)
# Constraints
model.KeineAhnung = Constraint(expr=((model.x / model.length)**2) +
((model.y / model.width)**2) - 1 >= 0)
model.pprint()
model.skip_canonical_repn = True # for nonlinear models
instance = model.create()
SolverName = "asl"
so = Bunch()
so.solver = "ipopt"
opt = SolverFactory(SolverName, options=so)
if opt is None:
print("Could not construct solver %s : %s" % (SolverName, so.solver))
sys.exit(1)
results = opt.solve(instance)
results.write()
instance.load(results) # put results in model
# because we know there is a variable named x
x_var = getattr(instance, "x")
x_val = x_var()
print("x was " + str(x_val))
# Mimic the pyomo script from pyomo.core import * from pyomo.common.collections import Bunch # set high level options that mimic pyomo comand line options = Bunch() options.model_file = 'DiseaseEstimation.py' options.data_files = ['DiseaseEstimation.dat'] options.solver = 'ipopt' options.solver_io = 'nl' #options.keepfiles = True #options.tee = True # mimic the set of function calls done by pyomo command line scripting.util.setup_environment(options) # the following imports the model found in options.model_file, # sets this to options.usermodel, and executes preprocessors scripting.util.apply_preprocessing(options, parser=None) # create the wrapper for the model, the data, the instance, and the options model_data = scripting.util.create_model(options) instance = model_data.instance # solve results, opt = scripting.util.apply_optimizer(options, instance) # the following simply outputs the final time elapsed scripting.util.finalize(options) # load results into instance and print
