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Re: [Help-glpk] [Fwd: Tricks with MathProg to approximate non-linear fun
From: |
Nigel Galloway |
Subject: |
Re: [Help-glpk] [Fwd: Tricks with MathProg to approximate non-linear functions?] |
Date: |
Sun, 07 May 2017 05:25:47 -0700 |
The Pearson Correlation can be obtained from Least Square analysis, so
you may find yalsm.mod in the glpk examples useful.
--
Nigel Galloway
address@hidden
On Fri, May 5, 2017, at 09:28 PM, Andrew Makhorin wrote:
> -------- Forwarded Message --------
> From: Dolan Antenucci <address@hidden>
> To: address@hidden
> Subject: Tricks with MathProg to approximate non-linear functions?
> Date: Fri, 05 May 2017 16:37:43 +0000
>
> I'm attempting to use GLPK to solve a problem with a non-linear
> objective function. Specifically, I want to use either a Pearson
> correlation coefficient
> (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) or
> something similar to the F1 score metric
> (https://en.wikipedia.org/wiki/F1_score).
>
>
>
> I know that GLPK is restricted to *linear* programming, but I'm
> wondering if there is a trick to representing either of these objectives
> as linear functions.
>
>
> I got some hope when I came across a guide for "MIP linearizations and
> formulations" from FICO
> (http://www.fico.com/en/node/8140?file=5125), which talks about
> approximating non-linear functions with a piecewise linear function, but
> since it is in regards to their Xpress Optimization Suite, I wasn't sure
> how this applied to my case or with GLPK.
>
>
> Are there any known tricks with GLPK for what I'm trying to do, or am I
> best off just choosing a linear objective function?
>
>
> Best Regards,
> Dolan Antenucci
>
>
>
>
>
>
>
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> Help-glpk mailing list
> address@hidden
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