Unable to find suboptimal solution
AnsweredI am trying to run Gurobi with a cutoff time and would like to see the best solution found so far. To do this I set the following parameters:
model.Params.timeLimit = cutoff
model.Params.MIPFocus = 1
model.optimize()
print(model.SolCount)
But I get 0 solutions even after 1 hour, is that normal, or am I doing something normal.

Thanks for the quick reply. Here is the log from a run with a 10 minutes cutoff:
Changed value of parameter timeLimit to 600.0
Prev: inf Min: 0.0 Max: inf Default: inf
Changed value of parameter MIPFocus to 1
Prev: 0 Min: 0 Max: 3 Default: 0
Gurobi Optimizer version 9.1.1 build v9.1.1rc0 (linux64)
Thread count: 12 physical cores, 24 logical processors, using up to 24 threads
Optimize a model with 233840 rows, 62627 columns and 523280 nonzeros
Model fingerprint: 0x50986aa2
Variable types: 9679 continuous, 52948 integer (52948 binary)
Coefficient statistics:
Matrix range [1e+00, 4e+01]
Objective range [1e+00, 1e+00]
Bounds range [1e+00, 2e+01]
RHS range [1e02, 4e+01]
Presolve removed 100600 rows and 41688 columns (presolve time = 5s) ...
Presolve removed 100603 rows and 41688 columns
Presolve time: 6.34s
Presolved: 133237 rows, 20939 columns, 397904 nonzeros
Variable types: 8099 continuous, 12840 integer (12840 binary)
Deterministic concurrent LP optimizer: primal and dual simplex (primal and dual model)
Showing first log only...Presolve removed 40 rows and 20 columns
Presolved: 133197 rows, 20919 columns, 397824 nonzeros
Root simplex log...Iteration Objective Primal Inf. Dual Inf. Time
0 0.0000000e+00 1.513000e+03 1.484300e+10 8s
14087 2.6518328e01 0.000000e+00 3.272363e+04 10s
17250 3.8408577e01 0.000000e+00 5.930891e+04 15s
19320 4.6563990e01 0.000000e+00 9.876198e+03 20s
Concurrent spin time: 4.23sSolved with dual simplex (dual model)
Root relaxation: objective 1.580000e+03, 61090 iterations, 17.65 seconds
Nodes  Current Node  Objective Bounds  Work
Expl Unexpl  Obj Depth IntInf  Incumbent BestBd Gap  It/Node Time0 0 1580.00000 0 247  1580.00000   29s
0 0 1580.00000 0 343  1580.00000   34s
0 0 1580.00000 0 294  1580.00000   37s
0 0 1580.00000 0 268  1580.00000   42s
0 0 1580.00000 0 221  1580.00000   45s
0 0 1580.00000 0 293  1580.00000   47s
0 0 1580.00000 0 295  1580.00000   51s
0 0 1580.00000 0 249  1580.00000   61s
0 0 1580.00000 0 281  1580.00000   66s
0 0 1580.00000 0 318  1580.00000   73s
0 2 1580.00000 0 318  1580.00000   228s
1 4 1580.00000 1 310  1580.00000  5769 230s
27 40 1580.00000 5 307  1580.00000  765 235s
87 101 1579.00000 9 286  1580.00000  539 251s
100 149 1575.00000 10 912  1580.00000  1029 265s
148 279 1578.00000 10 333  1580.00000  824 416s
278 734 1577.00000 12 357  1580.00000  972 485s
733 1620 1577.00000 34 401  1580.00000  671 593s
1659 1691 1569.43808 58 483  1580.00000  698 600sCutting planes:
Cover: 1
Implied bound: 82
Clique: 39
MIR: 11
Flow cover: 331
GUB cover: 1
Zero half: 24
RLT: 44
Relaxandlift: 3Explored 1730 nodes (1295080 simplex iterations) in 600.10 seconds
Thread count was 24 (of 24 available processors)Solution count 0
Time limit reached
Best objective , best bound 1.580000000000e+03, gap 
0 9 
Hi,
Thanks for posting your log.
The model is not extremely big and the numerics seems all right. From the log, you can see that presolve and the root relaxation are quite fast, but then Gurobi struggles to find feasible solutions. In fact, the dashes () in the Incumbent (position 6) and Gap (position 8) columns of the tree search log indicate that no feasible solution has been found yet. Looking at the bound (BestBd, position 7), you can see that the bound doesn't make progress either. So the model really seems to be quite hard.
Do you happen to know any feasible solution that you can pass as a MIP start?
You could try different parameter settings. Sometimes the zero objective, the minimum relaxation, the feasibility pump, or the no relaxation heuristics can help find an initial feasible solution. So you may experiment with setting/increasing the parameters I linked.
You could also try Gurobi's parameter tuning tool to find better parameter settings.
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