<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Sequential Descent Method for Global Optimization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2011.04866/</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/2011.04866/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Sequential Descent Method for Global Optimization — mentioned by GistNoesis</title><link>https://news.ycombinator.com/item?id=45629571</link><guid isPermaLink="false">urn:hackerpapers:mention:2011.04866:45629571</guid><pubDate>Sat, 18 Oct 2025 18:53:06 GMT</pubDate><description>&lt;p&gt;The general technique is not recent I was taught it in school in global optimisation class more than 15 years ago. Here there is a small number of local minimum, the idea is to iterate over them in increasing order. Can&amp;#x27;t remember the exact name but here is a more recent paper proposing &amp;quot;Sequential Gradient Descent&amp;quot; https://arxiv.org/abs/2011.04866 which features a similar idea. Sequential convex programming : http://web.stanford.edu/class/ee364b/lectures/seq_notes.pdf There is not really something special to it, it just standard local non linear minimization techniques with constraints…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2011.04866/"&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>
