I wish to use Pyomo and Bonmin in an operations research seminar concerning the "Extended Supporting Hyperplane Algorithm" from Kronqvist. I wish to use bonmin to compare the performance of the algorithm. First I want to test, whether Bonmin is installed correctly and it is working in together with pyomo, so I modeled an example from the paper mentioned above. It is a convex problem, so bonmin should be able to solve it.
My OS is Windows 7, so I installed Bonmin using Cygwin and added it to my PATh. I did not notice any error message during the installation. For Programming I am using Anaconda with Jupyter Notebook.
I am currently reading through the pyomos documentation, but did not have any luck so far.
from pyomo.environ import *
import numpy
import scipy
# Konkretes Optimierungsproblem
model = ConcreteModel(name = "Example 1")
model.x1 = Var(bounds=(1,20), within=Reals)
model.x2 = Var(bounds=(1,20), within=Integers)
model.obj = Objective(expr=(-1)*model.x1-model.x2)
model.g1 = Constraint(expr=0.15*((model.x1 - 8)**2)+0.1*((model.x2 - 6)**2)+0.025*exp(model.x1)*((model.x2)**(-2))-5<=0)
model.g2 = Constraint(expr=(model.x1)**(-1) + (model.x2)**(-1) - (model.x1)**(-0.5) * (model.x2) ** (-0.5)+4<=0)
model.l1 = Constraint(expr=2 * (model.x1) - 3 * (model.x2) -2<=0)
#Just some output to analze the generated model
print(model)
dir(model)
print(model.g2.expr)
model.x1 = 5
print(value(model.x1))
opt = SolverFactory('bonmin')
#opt.options['bonmin.algorithm'] = 'Bonmin'
print('using Bonmin')
# Set Options for solver.
opt.options['bonmin.solution_limit'] = '1'
opt.options['bonmin.time_limit'] = 1800
results = opt.solve(model)
results.write()
These are the results:
using Bonmin
WARNING: Loading a SolverResults object with a warning status into
model=Example 1;
message from solver=bonmin\x3a Infeasible problem
# ==========================================================
# = Solver Results =
# ==========================================================
# ----------------------------------------------------------
# Problem Information
# ----------------------------------------------------------
Problem:
- Lower bound: -inf
Upper bound: inf
Number of objectives: 1
Number of constraints: 0
Number of variables: 0
Sense: unknown
# ----------------------------------------------------------
# Solver Information
# ----------------------------------------------------------
Solver:
- Status: warning
Message: bonmin\x3a Infeasible problem
Termination condition: infeasible
Id: 220
Error rc: 0
Time: 0.16000056266784668
# ----------------------------------------------------------
# Solution Information
# ----------------------------------------------------------
Solution:
- number of solutions: 0
number of solutions displayed: 0
As you can see, the model ist not transferred to the solver correctly, since according to the solver the problem has neither constraints nor variables.
Could the problem be the warning stauts of the Solver meaning it is not installed correctly or is this due to the problem being infeasible?
Edit: As suggested by Bethany Nicholson in the comments, I added the option tee=tree in the code, which lead to the following output
using Bonmin
Bonmin 1.8.7 using Cbc 2.10.0 and Ipopt 3.12.12
bonmin: bonmin.solution_limit=1
bonmin.time_limit=1800
bonmin.solution_limit=1
bonmin.time_limit=1800
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
******************************************************************************
NLP0012I
Num Status Obj It time Location
NLP0014I 1 INFEAS 4.0986776 27 0.031
NLP0014I 2 INFEAS 4.0986776 27 0.015
Cbc0006I The LP relaxation is infeasible or too expensive
"Finished"
WARNING: Loading a SolverResults object with a warning status into
model=Example 1;
message from solver=bonmin\x3a Infeasible problem
# ==========================================================
# = Solver Results =
# ==========================================================
# ----------------------------------------------------------
# Problem Information
# ----------------------------------------------------------
Problem:
- Lower bound: -inf
Upper bound: inf
Number of objectives: 1
Number of constraints: 0
Number of variables: 0
Sense: unknown
# ----------------------------------------------------------
# Solver Information
# ----------------------------------------------------------
Solver:
- Status: warning
Message: bonmin\x3a Infeasible problem
Termination condition: infeasible
Id: 220
Error rc: 0
Time: 0.23000025749206543
# ----------------------------------------------------------
# Solution Information
# ----------------------------------------------------------
Solution:
- number of solutions: 0
number of solutions displayed: 0
Edit 2: I have tried other simple problems. While the problem is solved correctly (x1=4, x2=2, minvalue=-10) by Bonmin, its output states that the problems passed to the solver has no constraints, when it clearly has five (even though four might be interpreted as bounds). Furthermore, the output still appears a little strange to me. Why does it state "number of solutions = 0"? (I am still not through with the complete documentation of pyomo, maybe I just have to set additional parameters) Here is the linear problem:
from pyomo.environ import *
import numpy
import scipy
model = ConcreteModel(name = "Linear problem")
model.x1 = Var(domain = Reals)
model.x2 = Var(domain = Reals)
model.obj = Objective(expr=-1*(2*model.x1+model.x2))
model.l1 = Constraint(expr=model.x1>=0)
model.l2 = Constraint(expr=model.x2>=0)
model.l3 = Constraint(expr=model.x1<=4)
model.l4 = Constraint(expr=model.x2<=4)
model.l5 = Constraint(expr=model.x1+model.x2<=6)
model.pprint()
2 Var Declarations
x1 : Size=1, Index=None
Key : Lower : Value : Upper : Fixed : Stale : Domain
None : None : None : None : False : True : Reals
x2 : Size=1, Index=None
Key : Lower : Value : Upper : Fixed : Stale : Domain
None : None : None : None : False : True : Reals
1 Objective Declarations
obj : Size=1, Index=None, Active=True
Key : Active : Sense : Expression
None : True : minimize : - (2*x1 + x2)
5 Constraint Declarations
l1 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 0.0 : x1 : +Inf : True
l2 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 0.0 : x2 : +Inf : True
l3 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : -Inf : x1 : 4.0 : True
l4 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : -Inf : x2 : 4.0 : True
l5 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : -Inf : x1 + x2 : 6.0 : True
8 Declarations: x1 x2 obj l1 l2 l3 l4 l5
opt = SolverFactory('bonmin')
opt.options['bonmin.solution_limit'] = '1'
opt.options['bonmin.time_limit'] = 1800
results = opt.solve(model, tee = True)
results.write()
Bonmin 1.8.7 using Cbc 2.10.0 and Ipopt 3.12.12
bonmin: bonmin.solution_limit=1
bonmin.time_limit=1800
bonmin.solution_limit=1
bonmin.time_limit=1800
Cbc3007W No integer variables - nothing to do
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
******************************************************************************
NLP0012I
Num Status Obj It time Location
NLP0014I 1 OPT -10 4 0
Cbc3007W No integer variables - nothing to do
"Finished"
# ==========================================================
# = Solver Results =
# ==========================================================
# ----------------------------------------------------------
# Problem Information
# ----------------------------------------------------------
Problem:
- Lower bound: -inf
Upper bound: inf
Number of objectives: 1
Number of constraints: 0
Number of variables: 2
Sense: unknown
# ----------------------------------------------------------
# Solver Information
# ----------------------------------------------------------
Solver:
- Status: ok
Message: bonmin\x3a Optimal
Termination condition: optimal
Id: 3
Error rc: 0
Time: 0.20000028610229492
# ----------------------------------------------------------
# Solution Information
# ----------------------------------------------------------
Solution:
- number of solutions: 0
number of solutions displayed: 0
I am sorry if I appear to be spamming my post with code. Just thought it would be helpful, since I think this indicated that the problem lies with the solver rather than the modelling with pyomo.
I had a meeting with my tutor yesterday. I actually overlooked an error in the model, which made it infeasible. Bonmin solves the problem correctly, but the output still looks strange (e.g. model possesses no constraints). He does understand the somewhat strange output of the solver either, however, he said I should not concern myself with it any further.
I deeply apologize for doing such a stupid mistake. Hopefully no one spent much time thinking on this question (except me of course).