How to solve the optimization problem using pyomo.opt

[xiwang...@gmail.com]

Dear all,

 

I am trying to repeat the pyomo.opt example in section 18.1 of PyomoOnlineDocs, I used the pyomo.opt created the model: RealProblem1:

 

from pyomo.environ import *
import sys
from pyomo.opt.blackbox import RealOptProblem

 

class RealProblem1(RealOptProblem):
 def __init__(self):
  RealOptProblem.__init__(self)
  self.lower=[0.0, -1.0, 1.0, None]
  self.upper=[None, 0.0, 2.0, -1.0]
  self.nvars=4
  
 def function_value(self, point):
  self.validate(point)
  return point.vars[0] - point.vars[1] + (point.vars[2]-1.5)**2 + (point.vars[3]+2)**4

 

But I can I use the solver to solve this problem. Since I did not declare the "model", "objective", when I ran "pyomo solve RealProblem1.py --solver=glpk, there will ba an error: name 'model' is not defined.

 

So I am wandering how can I declare the model, objective and solve the problem using Pyomo.opt.

 

Thanks very much,

 

Xiwang

[Patricio Reyes]

Hello,

I have the same question...

I can't find a full example using `pyomo.opt.blackbox`. (I already read the [documentation](https://software.sandia.gov/downloads/pub/pyomo/PyomoOnlineDocs.html#_defining_and_optimizing_simple_black_box_applications) but I don't know what are the next steps...)

Do you have more info?

Thanks

[Siirola, John D]

The long and the short of it is that the blackbox functionality is orthogonal to the Pyomo modeling language (it is a holdover from the early days of Coopr).  It was an attempt to make setting up models for use with a specific solver suite (coliny – part of the Acro project) easier by allowing the models to be defined in Python.  pyomo.opt.blackbox is not compatible with the pyomo script, nor with the general solvers that are interfaced through Pyomo.

 

There is some movement in the developer team to extend the current external function interface in Pyomo to support more general blackbox models, however that is in its early stages and will not likely make it into a release until late 2017 at the earliest.

 

john

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