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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
> https://lists.gnu.org/mailman/listinfo/help-glpk

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