Any good practice for using bigM?
AnsweredHi,
I am using many big-Ms in my model, which makes my coefficient statistics very bad. I think that is the main reason for the very slow solving of my optimisation.
Matrix range [2e-02, 4e+06]
Objective range [1e+00, 2e+02]
Bounds range [1e+00, 1e+04]
RHS range [1e+00, 4e+06]
Is there any method to avoid using bigMs, or if I have to, how can I avoid numerical issues caused by bigM?
Thanks.
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Hi Yongning,
in general, I can recommend to you this blog post by Paul Rubin on the subject, it covers a lot of important aspects.
Beyond that, you could try to "calculate" your tightest big-M for a given model. So suppose you use a big-M formulation
such as this:
```
t <= y*M
```
Then, you could find out what the largest value is that `t` can attain. You can do this by solving the model with the objective function:
```
max. t
```
This will then return to you the biggest value that the variable t can have, and consequently the smallest value that M can have. This of course only works for this particular model; if your data changes, this value may change. However, it can (a) give you an indication on how "good" your big-M value is and (b) inspire you to find ways to tighten the formulation.
Hope this helps.
Best
Richard
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Many thanks, Richard. That's quite informative and useful.
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