<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2401.03244/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2401.03244/feed.xml" rel="self" type="application/rss+xml" /><item><title>Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process — mentioned by l33t7332273</title><link>https://news.ycombinator.com/item?id=41544949</link><guid isPermaLink="false">urn:hackerpapers:mention:2401.03244:41544949</guid><pubDate>Sun, 15 Sep 2024 03:21:01 GMT</pubDate><description>&lt;p&gt;I was referring to section 4 of A Survey for Solving Mixed Integer Programming via Machine Learning(2024): https://arxiv.org/pdf/2401.03244. I’ve heard (but not so much observed) that there is substantial difference between recent models, so it’s possible that they are better than when this was written. Anyways, CPLEX has an associated modeling language that features syntactic sugar which has the effect of providing opaqueness to the underlying MILP that it solves. I find LLMs essentially unable to even make an attempt at determining the MILP from that language. PS: How is Xpress? Is there…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2401.03244/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>math.OC</category></item></channel></rss>
