How to linearize a weighted average with a decision variable?
Matt <[email protected]>
| Newsgroups | gmane.comp.gnu.glpk |
|---|---|
| Message-ID | <CA+jyAG816b0_pUstfenrrY2=Afw0AN_Oj0PVzCP-hKh+Y-9hOw@mail.gmail.com> |
Hi all,
I'm trying to model a problem but it turned out to be non linear.
A simplified version of the model is written below. Basically it averages
the weighted value of all enabled points, provided there are exactly M
enabled points.
*max sum(i) { enabled[i] * value[i] * weight[i] } / sum(i) { enabled[i] *
weight[i] }*
*s.t. sum (i) enabled[i] = M*
- *value* is a vector of decimal numbers in [0, 1] (precomputed)
- *weight* is a vector of decimal numbers in [0, 1] (precomputed)
- *enabled* is a vector of either 0 or 1 (decision variable)
The model is very simple so I'm guessing there probably is a way to
linearize it or some workaround I'm not aware of.
Thanks,
Matt
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