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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Stochastic Gradient Descent Works Really Well for Stress Minimization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2008.10376/</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/2008.10376/feed.xml" rel="self" type="application/rss+xml" /><item><title>Stochastic Gradient Descent Works Really Well for Stress Minimization — mentioned by graphviz</title><link>https://news.ycombinator.com/item?id=29973089</link><guid isPermaLink="false">urn:hackerpapers:mention:2008.10376:29973089</guid><pubDate>Mon, 17 Jan 2022 23:55:27 GMT</pubDate><description>&lt;p&gt;Most of the work has been on CI/CD, Windows, rationalizing and modernizing the code, and Zheng, Pawar and Goodman contributed a stochastic gradient descent solver, described here, https://arxiv.org/abs/1710.04626 with benefits pointed out by Börsig, Brandes and Pasztor https://arxiv.org/abs/2008.10376 but it&amp;#x27;s not a default solver, you&amp;#x27;d have to ask for it. Great people joined (took over?) the project and have done an amazing job of triage on open bugs, and some were even fixed. We rewrote parts of the network simplex solver to eliminate some of the dumbfounding &amp;quot;trouble in init_rank&amp;quot; errors.…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2008.10376/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CG</category></item></channel></rss>
