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personal:blog:2014:0904_pyomo_for_gams_users [2015/01/12 14:34] – [Complete script] antonellopersonal:blog:2014:0904_pyomo_for_gams_users [2025/05/02 09:41] (current) – external edit 127.0.0.1
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 ====== A Pyomo tutorial for GAMS users ====== ====== A Pyomo tutorial for GAMS users ======
 +
 +**Updates:**
 +
 +  * **2015.01.12: Updated to run under Pyomo 4. See previous revisions if you still use Pyomo 3.**
 +
 +
  
 [[https://software.sandia.gov/trac/coopr|Pyomo]] (Python Optimisation Modeling Object) is an Algebraic Modelling Language (AML) that allows to write optimisation problems using a concise mathematical formulation, acting as interface to the specific solver engine API. For non-linear optimisation problems it allows to keep a high-level approach that doesn't require the modeller to compute the Jacobian or the Hessian.\\ [[https://software.sandia.gov/trac/coopr|Pyomo]] (Python Optimisation Modeling Object) is an Algebraic Modelling Language (AML) that allows to write optimisation problems using a concise mathematical formulation, acting as interface to the specific solver engine API. For non-linear optimisation problems it allows to keep a high-level approach that doesn't require the modeller to compute the Jacobian or the Hessian.\\
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 ===== Installation ===== ===== Installation =====
  
-**Important: Pyomo requires python 2.x. While python 3.x support is work in progress, at the moment only python 2.x is supported.**+**<del>Important: Pyomo requires python 2.x. While python 3.x support is work in progress, at the moment only python 2.x is supported.</del>** 
 + 
 +//This isn't true any more with Pyomo 4, where support for Python 3.x has been added..//
  
 ==== Ubuntu ==== ==== Ubuntu ====
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 <code python> <code python>
 # Import of the pyomo module # Import of the pyomo module
-from coopr.pyomo import *+from pyomo.environ import *
                                  
 # Creation of a Concrete Model # Creation of a Concrete Model
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 def demand_rule(model, j): def demand_rule(model, j):
   return sum(model.x[i,j] for i in model.i) >= model.b[j]     return sum(model.x[i,j] for i in model.i) >= model.b[j]  
-model.demand = Constraint(model.j, doc='Satisfy demand at market j')+model.demand = Constraint(model.j, rule=demand_rule, doc='Satisfy demand at market j')
 </code> </code>
 The above code take advantage of [[https://docs.python.org/2/tutorial/datastructures.html#list-comprehensions|List Comprehensions]], a powerful feature of the python language that provides a concise way to loop over a list. The above code take advantage of [[https://docs.python.org/2/tutorial/datastructures.html#list-comprehensions|List Comprehensions]], a powerful feature of the python language that provides a concise way to loop over a list.
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 If you want advanced features and debugging capabilities you can use a dedicated Python IDE, like e.g. [[https://code.google.com/p/spyderlib/|Spyder]]. If you want advanced features and debugging capabilities you can use a dedicated Python IDE, like e.g. [[https://code.google.com/p/spyderlib/|Spyder]].
  
-You will normally run the script as ''pyomo --solver=glpk transport.py''. You can output solver specific output adding the option ''--stream-output''.\\+You will normally run the script as ''pyomo solve --solver=glpk transport.py''. You can output solver specific output adding the option ''--stream-output''.\\
 If you want to run the script as ''python transport.py'' add the following lines at the end:\\ If you want to run the script as ''python transport.py'' add the following lines at the end:\\
 <code python> <code python>
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 if __name__ == '__main__': if __name__ == '__main__':
     #This replicates what the pyomo command-line tools does     #This replicates what the pyomo command-line tools does
-    from coopr.opt import SolverFactory +    from pyomo.opt import SolverFactory 
-    import coopr.environ+    import pyomo.environ
     opt = SolverFactory("glpk")     opt = SolverFactory("glpk")
     instance = model.create()     instance = model.create()
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