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How can I retrieve the time used and relative MIP gap after optimizing with GLPK in Pyomo?
2019 Community Moderator ElectionHow to set glpk solver parameters in openoptModeled an MIP woth Pyomo in Python, cannot solve it with GurobiPython GLPK - Appdata write - Permission deniedHow to interface Pyomo with GLPK?Pyomo takes time after IPOPT solver call?Pyomo scaling with GLPKPyomo: ERROR: evaluating expression: No value for uninitialized NumericValue objectHow to set a time limit or relaxation parameter for objective function using GLPK in Python?Pyomo Solver Windows pop-upHow to set a constraint depending on previous set index
I am using a concrete model with Pyomo (using GLPK) where two optimizations are run for each day of the year, but during test runs (just a couple of days) the MIP gap is sometimes very high (around 8%) after the time limit. I wish to store (in a dataframe) the time used for each optimization and the MIP gap that was reached, so I can get an idea of a) how long does the average optimization take, and b) how close the results are to optimality. How can I retrieve this data? I haven't been able to find a way to do this.
The code and the data used are too long to share, but this is how I am calling the solver and giving a time limit and gap criteria:
model = create_model(parameters_a, parameters_b)
solver = SolverFactory('glpk')
solver.options["mipgap"] = 0.01
solver.options["tmlim"] = 1000
solver.solve(model, tee=True, symbolic_solver_labels=False)
pyomo glpk
asked Mar 7 at 13:57
BenjaminBenjamin
11
add a comment |
I am using a concrete model with Pyomo (using GLPK) where two optimizations are run for each day of the year, but during test runs (just a couple of days) the MIP gap is sometimes very high (around 8%) after the time limit. I wish to store (in a dataframe) the time used for each optimization and the MIP gap that was reached, so I can get an idea of a) how long does the average optimization take, and b) how close the results are to optimality. How can I retrieve this data? I haven't been able to find a way to do this.
The code and the data used are too long to share, but this is how I am calling the solver and giving a time limit and gap criteria:
model = create_model(parameters_a, parameters_b)
solver = SolverFactory('glpk')
solver.options["mipgap"] = 0.01
solver.options["tmlim"] = 1000
solver.solve(model, tee=True, symbolic_solver_labels=False)
pyomo glpk
asked Mar 7 at 13:57
BenjaminBenjamin
11
add a comment |
I am using a concrete model with Pyomo (using GLPK) where two optimizations are run for each day of the year, but during test runs (just a couple of days) the MIP gap is sometimes very high (around 8%) after the time limit. I wish to store (in a dataframe) the time used for each optimization and the MIP gap that was reached, so I can get an idea of a) how long does the average optimization take, and b) how close the results are to optimality. How can I retrieve this data? I haven't been able to find a way to do this.
The code and the data used are too long to share, but this is how I am calling the solver and giving a time limit and gap criteria:
model = create_model(parameters_a, parameters_b)
solver = SolverFactory('glpk')
solver.options["mipgap"] = 0.01
solver.options["tmlim"] = 1000
solver.solve(model, tee=True, symbolic_solver_labels=False)
pyomo glpk
asked Mar 7 at 13:57
BenjaminBenjamin
11
I am using a concrete model with Pyomo (using GLPK) where two optimizations are run for each day of the year, but during test runs (just a couple of days) the MIP gap is sometimes very high (around 8%) after the time limit. I wish to store (in a dataframe) the time used for each optimization and the MIP gap that was reached, so I can get an idea of a) how long does the average optimization take, and b) how close the results are to optimality. How can I retrieve this data? I haven't been able to find a way to do this.
The code and the data used are too long to share, but this is how I am calling the solver and giving a time limit and gap criteria:
model = create_model(parameters_a, parameters_b)
solver = SolverFactory('glpk')
solver.options["mipgap"] = 0.01
solver.options["tmlim"] = 1000
solver.solve(model, tee=True, symbolic_solver_labels=False)
pyomo glpk
pyomo glpk
asked Mar 7 at 13:57
BenjaminBenjamin
11
asked Mar 7 at 13:57
BenjaminBenjamin
11
asked Mar 7 at 13:57
BenjaminBenjamin
11
asked Mar 7 at 13:57
BenjaminBenjamin
11
asked Mar 7 at 13:57
BenjaminBenjamin
11
11
add a comment |
add a comment |
1 Answer
1
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votes
Python has various utilities for timing, which may be useful. You can also check the contents of the return object from the solve() call:
import time
start = time.time()
results = solver.solve(model, tee=True)
print(results)
end = time.time()
print(end - start)
answered Mar 8 at 17:05
Qi ChenQi Chen
1,050616
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Python has various utilities for timing, which may be useful. You can also check the contents of the return object from the solve() call:
import time
start = time.time()
results = solver.solve(model, tee=True)
print(results)
end = time.time()
print(end - start)
answered Mar 8 at 17:05
Qi ChenQi Chen
1,050616
add a comment |
Python has various utilities for timing, which may be useful. You can also check the contents of the return object from the solve() call:
import time
start = time.time()
results = solver.solve(model, tee=True)
print(results)
end = time.time()
print(end - start)
answered Mar 8 at 17:05
Qi ChenQi Chen
1,050616
add a comment |
Python has various utilities for timing, which may be useful. You can also check the contents of the return object from the solve() call:
import time
start = time.time()
results = solver.solve(model, tee=True)
print(results)
end = time.time()
print(end - start)
answered Mar 8 at 17:05
Qi ChenQi Chen
1,050616
Python has various utilities for timing, which may be useful. You can also check the contents of the return object from the solve() call:
import time
start = time.time()
results = solver.solve(model, tee=True)
print(results)
end = time.time()
print(end - start)
answered Mar 8 at 17:05
Qi ChenQi Chen
1,050616
answered Mar 8 at 17:05
Qi ChenQi Chen
1,050616
answered Mar 8 at 17:05
Qi ChenQi Chen
1,050616
answered Mar 8 at 17:05
Qi ChenQi Chen
1,050616
1,050616
add a comment |
add a comment |
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