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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Global Convergence of ADMM in Nonconvex Nonsmooth Optimization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1511.06324/</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/1511.06324/feed.xml" rel="self" type="application/rss+xml" /><item><title>Global Convergence of ADMM in Nonconvex Nonsmooth Optimization — mentioned by ingenieroariel</title><link>https://news.ycombinator.com/item?id=22657638</link><guid isPermaLink="false">urn:hackerpapers:mention:1511.06324:22657638</guid><pubDate>Sun, 22 Mar 2020 19:09:44 GMT</pubDate><description>&lt;p&gt;These kind of geometrical solutions are easier to grasp than other traditional solutions. If geometry is not your thing but programming is, I really recommend Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares [0] by Stanford&amp;#x27;s prof Boyd, he has written about fast methods using linear algebra that are guaranteed to converge if they meet certain criteria [1]. [0] http://vmls-book.stanford.edu/ [1] https://arxiv.org/abs/1511.06324&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1511.06324/"&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>
