Re: [Fwd: Tricks with MathProg to approximate non-linear functions?]
Nigel Galloway <[email protected]>
| Newsgroups | gmane.comp.gnu.glpk |
|---|---|
| Message-ID | <[email protected]> |
The Pearson Correlation can be obtained from Least Square analysis, so you may find yalsm.mod in the glpk examples useful. -- Nigel Galloway [email protected] On Fri, May 5, 2017, at 09:28 PM, Andrew Makhorin wrote: > -------- Forwarded Message -------- > From: Dolan Antenucci <[email protected]> > To: [email protected] > 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 > > > > > > > > _______________________________________________ > Help-glpk mailing list > [email protected] > https://lists.gnu.org/mailman/listinfo/help-glpk -- http://www.fastmail.com - IMAP accessible web-mail