Tips for speeding up MIP solution process when the warm-start point is almost always optimal
回答済みDear All,
I am trying to solve a MIP in Gurobi. For this particular problem, I have a feasible point that is globally optimal for the MIP. So, essentially when I am warm-starting with this point, Gurobi's job is just proving the optimality of the given solution. I am currently setting `MIPfocus = 2` to that goal, and Gurobi indeed verifies that the warm-starting point is optimal. However, it does not speed up the process by a significant margin compared to if I had started from scratch without warm-starting. Are there any other things that I can do to increase the speed of Gurobi in verifying the optimality of the given solution?
Any tips/suggestions will be much appreciated.
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Hi,
When you are sure that your point is already optimal, you could turn off all heuristics by setting Heuristics=0 and RINS=0. Additionally, you could set MIPFocus=3 to make Gurobi focus solely on the lower bound. Could you post a LOG snippet of an optimization run?
Best regards,
Jaromił0 -
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Hi Jaromił,
Thanks very much for your quick response. Okay, I will turn off all heuristics and see what happens. Here is a Gurobi log of an optimization run (I am using Julia+JuMP to model the problem), where we see that the warm-started objective value and objective value of the found solution by Gurobi are the same. Please let me know if you have any other tips/suggestions.
Gurobi Optimizer version 9.1.2 build v9.1.2rc0 (win64)
Thread count: 4 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 56 rows, 72 columns and 174 nonzeros
Model fingerprint: 0xf0f3b6d9
Model has 40 quadratic constraints
Coefficient statistics:
Matrix range [5e-01, 1e+00]
QMatrix range [6e-05, 1e+00]
QLMatrix range [1e-03, 1e+00]
Objective range [1e+00, 1e+00]
Bounds range [1e+00, 3e+00]
RHS range [6e-01, 6e-01]
QRHS range [1e-02, 6e-01]
Presolve removed 31 rows and 16 columns
Continuous model is non-convex -- solving as a MIP.
Loaded user MIP start with objective 0.598515
Presolve removed 28 rows and 19 columns
Presolve time: 0.00s
Presolved: 222 rows, 96 columns, 619 nonzeros
Presolved model has 49 bilinear constraint(s)
Variable types: 96 continuous, 0 integer (0 binary)
Presolve removed 17 rows and 2 columns
Presolved: 205 rows, 94 columns, 599 nonzeros
Root relaxation: objective 1.066596e-02, 83 iterations, 0.00 seconds
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
0 0 0.01067 0 30 0.59851 0.01067 98.2% - 0s
0 0 0.01067 0 33 0.59851 0.01067 98.2% - 0s
0 0 0.01067 0 30 0.59851 0.01067 98.2% - 0s
0 0 0.01067 0 30 0.59851 0.01067 98.2% - 0s
0 0 0.01067 0 33 0.59851 0.01067 98.2% - 0s
0 2 0.01067 0 33 0.59851 0.01067 98.2% - 0s
1983 1644 0.42567 23 45 0.59851 0.13978 76.6% 86.8 5s
6783 3377 cutoff 24 0.59851 0.44084 26.3% 74.6 10s
13988 4812 0.57309 27 40 0.59851 0.51380 14.2% 58.6 15s
18800 5214 0.57482 30 45 0.59851 0.53415 10.8% 53.6 20s
24041 5425 infeasible 35 0.59851 0.55021 8.07% 49.8 25s
29999 6014 cutoff 36 0.59851 0.56241 6.03% 47.0 30s
36550 5675 0.57599 27 48 0.59851 0.57362 4.16% 44.8 35s
42884 4817 infeasible 36 0.59851 0.58310 2.58% 43.4 40s
48656 3199 0.59704 30 37 0.59851 0.59062 1.32% 42.1 45s
54854 1182 infeasible 28 0.59851 0.59725 0.21% 40.6 50s
60939 1988 cutoff 49 0.59851 0.59835 0.03% 38.9 55s
66155 3346 cutoff 46 0.59851 0.59845 0.01% 37.6 60s
Cutting planes:
RLT: 15
PSD: 678
Explored 68053 nodes (2522831 simplex iterations) in 60.72 seconds
Thread count was 8 (of 8 available processors)
Solution count 1: 0.598515
Optimal solution found (tolerance 1.00e-04)
Best objective 5.985146520952e-01, best bound 5.984600770353e-01, gap 0.0091%0 -
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Hi,
Since the model solves rather quickly, you could try the Gurobi Parameter Tuning Tool. You could let it run for a day and check whether there are some parameters which might help. If no parameters can be found which produce a significant improvement, then the best way would be to try to improve variable bounds and try a reformulation of the problem.
Best regards,
Jaromił0 -
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Great, thanks so much!
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