Understanding log-files produced by Bonmin
I want to obtain a feasible point for a MINLP using the feasibility pump implemented in Bonmin which I access via Pyomo (I use the parameter SolutionLimit = 1).
For some problems, however, the method does not yield such a point within a time limit of 30 minutes and I wish to determine the reason.
Looking at the log-files, it appears that the reason is that the first auxiliary MILP is not being solved with Cbc. In fact, for the problem du-opt from the MINLPLib, after some time there appears to be no more progess in the solution of the MILP(?), and the following lines are repeated in the log:
OCbc0014I Cut generator 0 (Probing) - 0 row cuts average 0.0 elements,
0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0014I Cut generator 1 (Gomory) - 1 row cuts average 20.0 elements,
0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0
elements, 0 column cuts (0 active) in 0.000 seconds - new frequency
is -100
OCbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements,
0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0014I Cut generator 4 (FlowCover) - 0 row cuts average 0.0
elements, 0 column cuts (0 active) in 0.000 seconds - new frequency
is -100
OCbc0014I Cut generator 5 (MixedIntegerRounding2) - 0 row cuts average
0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0010I After 0 nodes, 1 on tree, 1e+50 best solution, best possible
-189 (0.02 seconds)
OCbc0012I Integer solution of -189 found by rounding after 2 iterations and 1 nodes (0.02 seconds)
OCbc0001I Search completed - best objective -189, took 2 iterations
and 1 nodes (0.02 seconds)
OCbc0032I Strong branching done 4 times (10
iterations), fathomed 0 nodes and fixed 0 variables OCbc0035I Maximum
depth 0, 0 variables fixed on reduced cost
OCbc0031I 1 added rows had
average density of 20 OCbc0013I At root node, 1 cuts changed objective
from -189 to -189 in 2 passes
This coincides with the fact that the only source of nonlinearity for this problem stems from the objective function and hence the method should not have problems with finding any feasible point - except the initial MILP is difficult to solve. So basically, my questions are as follows.
- Do the problems du-opt as well as instances of the problem squfl have a structure which makes the projection problem of the feasibility pump hard to solve?
- Can anyone point me to a place where I can learn how to read the log-files?
- Is the Bonmin mailing list maybe an appropriate place to ask such a question?
pyomo coin-or-cbc
asked Nov 14 '18 at 10:05
Christoph NeumannChristoph Neumann
587
add a comment |
I want to obtain a feasible point for a MINLP using the feasibility pump implemented in Bonmin which I access via Pyomo (I use the parameter SolutionLimit = 1).
For some problems, however, the method does not yield such a point within a time limit of 30 minutes and I wish to determine the reason.
Looking at the log-files, it appears that the reason is that the first auxiliary MILP is not being solved with Cbc. In fact, for the problem du-opt from the MINLPLib, after some time there appears to be no more progess in the solution of the MILP(?), and the following lines are repeated in the log:
OCbc0014I Cut generator 0 (Probing) - 0 row cuts average 0.0 elements,
0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0014I Cut generator 1 (Gomory) - 1 row cuts average 20.0 elements,
0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0
elements, 0 column cuts (0 active) in 0.000 seconds - new frequency
is -100
OCbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements,
0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0014I Cut generator 4 (FlowCover) - 0 row cuts average 0.0
elements, 0 column cuts (0 active) in 0.000 seconds - new frequency
is -100
OCbc0014I Cut generator 5 (MixedIntegerRounding2) - 0 row cuts average
0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0010I After 0 nodes, 1 on tree, 1e+50 best solution, best possible
-189 (0.02 seconds)
OCbc0012I Integer solution of -189 found by rounding after 2 iterations and 1 nodes (0.02 seconds)
OCbc0001I Search completed - best objective -189, took 2 iterations
and 1 nodes (0.02 seconds)
OCbc0032I Strong branching done 4 times (10
iterations), fathomed 0 nodes and fixed 0 variables OCbc0035I Maximum
depth 0, 0 variables fixed on reduced cost
OCbc0031I 1 added rows had
average density of 20 OCbc0013I At root node, 1 cuts changed objective
from -189 to -189 in 2 passes
This coincides with the fact that the only source of nonlinearity for this problem stems from the objective function and hence the method should not have problems with finding any feasible point - except the initial MILP is difficult to solve. So basically, my questions are as follows.
- Do the problems du-opt as well as instances of the problem squfl have a structure which makes the projection problem of the feasibility pump hard to solve?
- Can anyone point me to a place where I can learn how to read the log-files?
- Is the Bonmin mailing list maybe an appropriate place to ask such a question?
pyomo coin-or-cbc
asked Nov 14 '18 at 10:05
Christoph NeumannChristoph Neumann
587
add a comment |
I want to obtain a feasible point for a MINLP using the feasibility pump implemented in Bonmin which I access via Pyomo (I use the parameter SolutionLimit = 1).
For some problems, however, the method does not yield such a point within a time limit of 30 minutes and I wish to determine the reason.
Looking at the log-files, it appears that the reason is that the first auxiliary MILP is not being solved with Cbc. In fact, for the problem du-opt from the MINLPLib, after some time there appears to be no more progess in the solution of the MILP(?), and the following lines are repeated in the log:
OCbc0014I Cut generator 0 (Probing) - 0 row cuts average 0.0 elements,
0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0014I Cut generator 1 (Gomory) - 1 row cuts average 20.0 elements,
0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0
elements, 0 column cuts (0 active) in 0.000 seconds - new frequency
is -100
OCbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements,
0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0014I Cut generator 4 (FlowCover) - 0 row cuts average 0.0
elements, 0 column cuts (0 active) in 0.000 seconds - new frequency
is -100
OCbc0014I Cut generator 5 (MixedIntegerRounding2) - 0 row cuts average
0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0010I After 0 nodes, 1 on tree, 1e+50 best solution, best possible
-189 (0.02 seconds)
OCbc0012I Integer solution of -189 found by rounding after 2 iterations and 1 nodes (0.02 seconds)
OCbc0001I Search completed - best objective -189, took 2 iterations
and 1 nodes (0.02 seconds)
OCbc0032I Strong branching done 4 times (10
iterations), fathomed 0 nodes and fixed 0 variables OCbc0035I Maximum
depth 0, 0 variables fixed on reduced cost
OCbc0031I 1 added rows had
average density of 20 OCbc0013I At root node, 1 cuts changed objective
from -189 to -189 in 2 passes
This coincides with the fact that the only source of nonlinearity for this problem stems from the objective function and hence the method should not have problems with finding any feasible point - except the initial MILP is difficult to solve. So basically, my questions are as follows.
- Do the problems du-opt as well as instances of the problem squfl have a structure which makes the projection problem of the feasibility pump hard to solve?
- Can anyone point me to a place where I can learn how to read the log-files?
- Is the Bonmin mailing list maybe an appropriate place to ask such a question?
pyomo coin-or-cbc
asked Nov 14 '18 at 10:05
Christoph NeumannChristoph Neumann
587
I want to obtain a feasible point for a MINLP using the feasibility pump implemented in Bonmin which I access via Pyomo (I use the parameter SolutionLimit = 1).
For some problems, however, the method does not yield such a point within a time limit of 30 minutes and I wish to determine the reason.
Looking at the log-files, it appears that the reason is that the first auxiliary MILP is not being solved with Cbc. In fact, for the problem du-opt from the MINLPLib, after some time there appears to be no more progess in the solution of the MILP(?), and the following lines are repeated in the log:
OCbc0014I Cut generator 0 (Probing) - 0 row cuts average 0.0 elements,
0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0014I Cut generator 1 (Gomory) - 1 row cuts average 20.0 elements,
0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0
elements, 0 column cuts (0 active) in 0.000 seconds - new frequency
is -100
OCbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements,
0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0014I Cut generator 4 (FlowCover) - 0 row cuts average 0.0
elements, 0 column cuts (0 active) in 0.000 seconds - new frequency
is -100
OCbc0014I Cut generator 5 (MixedIntegerRounding2) - 0 row cuts average
0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100
OCbc0010I After 0 nodes, 1 on tree, 1e+50 best solution, best possible
-189 (0.02 seconds)
OCbc0012I Integer solution of -189 found by rounding after 2 iterations and 1 nodes (0.02 seconds)
OCbc0001I Search completed - best objective -189, took 2 iterations
and 1 nodes (0.02 seconds)
OCbc0032I Strong branching done 4 times (10
iterations), fathomed 0 nodes and fixed 0 variables OCbc0035I Maximum
depth 0, 0 variables fixed on reduced cost
OCbc0031I 1 added rows had
average density of 20 OCbc0013I At root node, 1 cuts changed objective
from -189 to -189 in 2 passes
This coincides with the fact that the only source of nonlinearity for this problem stems from the objective function and hence the method should not have problems with finding any feasible point - except the initial MILP is difficult to solve. So basically, my questions are as follows.
- Do the problems du-opt as well as instances of the problem squfl have a structure which makes the projection problem of the feasibility pump hard to solve?
- Can anyone point me to a place where I can learn how to read the log-files?
- Is the Bonmin mailing list maybe an appropriate place to ask such a question?
pyomo coin-or-cbc
pyomo coin-or-cbc
asked Nov 14 '18 at 10:05
Christoph NeumannChristoph Neumann
587
asked Nov 14 '18 at 10:05
Christoph NeumannChristoph Neumann
587
asked Nov 14 '18 at 10:05
Christoph NeumannChristoph Neumann
587
asked Nov 14 '18 at 10:05
Christoph NeumannChristoph Neumann
587
asked Nov 14 '18 at 10:05
Christoph NeumannChristoph Neumann
587
587
add a comment |
add a comment |
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