Evaluating variability
CPLEX solves a problem multiple times to evaluate variability.
CPLEX can solve the same mixed integer problem multiple times, each time using a different random seed. Exercising this tool can serve as a surrogate for random perturbation of your model. In fact, exercising this tool can help you evaluate the variability of the class of problems that your instance belongs to.
Tip: Your problem must be one of the following types of problem:
- mixed integer linear program (MILP)
- mixed integer quadratic program (MIQP)
- mixed integer quadratically constrained program (MIQCP)
To invoke this procedure, follow these steps:
- First, enter or read your model, so that your problem is currently in memory and available to CPLEX.
- Optionally, set CPLEX parameters in the usual way; these settings will be applied on every run.
- Then invoke the following command, where n specifies the number of times that you want CPLEX to solve your problem for comparison.
tools runseeds n
If you do not specify n, CPLEX solves 30 times by default.
Sample log of a run to evaluate variability
exit sol objective gap iteration node runtime dettime
run code stat value (%) count count seconds ticks
1 0 108 --- --- 419468 39808 8.00 4195.20
2 0 108 --- --- 514998 49440 8.00 4369.46
3 0 101 0 0.00 69242 8453 1.55 981.88
4 0 108 --- --- 518059 37514 8.00 4460.28
5 0 101 0 0.00 123420 16923 4.52 3692.25
6 0 108 --- --- 511910 46768 8.01 4550.99
7 0 108 --- --- 459329 41168 8.00 4260.86
8 0 108 --- --- 431280 32714 8.01 4215.08
9 0 108 --- --- 379432 36441 8.00 3992.18
10 0 108 --- --- 377233 27091 8.01 3710.86
11 0 108 --- --- 333495 20436 8.01 3620.59
12 0 101 0 0.00 21160 2993 0.44 225.97
13 0 101 0 0.00 124943 14762 4.55 3714.96
14 0 101 0 0.00 113538 12581 4.43 3641.18
15 0 108 --- --- 549655 46617 8.00 4606.69
16 0 108 --- --- 447175 26129 8.00 4007.73
17 0 101 0 0.00 38622 5525 1.42 843.57
18 0 108 --- --- 413188 43561 8.00 4158.66
19 0 101 0 0.00 490580 41267 7.83 4324.97
20 0 108 --- --- 499872 38093 8.00 4394.23
21 0 101 0 0.00 292 0 0.14 86.63
22 0 108 --- --- 450731 47616 8.00 4373.14
23 0 101 0 0.00 101645 9091 1.57 982.50
24 0 108 --- --- 520977 50566 8.00 4412.67
25 0 108 --- --- 501371 45112 8.00 4371.53
26 0 108 --- --- 496808 39674 8.00 4352.48
27 0 108 --- --- 414917 44412 8.00 4345.97
28 0 101 0 0.00 427330 31227 6.97 4120.19
29 0 108 --- --- 481120 44447 8.00 4547.70
30 0 101 0 0.00 465275 40579 7.69 4222.06
Exit codes:
0 : No error
Optimization status codes:
objective gap iteration node runtime dettime
value (%) count count seconds ticks
101 : integer optimal solution (11 times)
average: 0 0.00 179641 16673 3.74 2439.65
std dev: 0 0.00 185898 14571 2.89 1772.79
108 : time limit exceeded, no integer solution (19 times)
average: --- --- 459001 39874 8.00 4260.33
std dev: --- --- 58792 8317 0.00 267.13