-------- 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
