<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Rank-Nullity on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/rank-nullity/</link><description>Recent content in Rank-Nullity on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Thu, 09 Jul 2026 21:02:12 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/rank-nullity/index.xml" rel="self" type="application/rss+xml"/><item><title>Linear Algebra</title><link>https://abaj.ai/wiki/mathematics/linear-algebra/</link><pubDate>Thu, 09 Jul 2026 21:02:56 +1000</pubDate><guid>https://abaj.ai/wiki/mathematics/linear-algebra/</guid><description>&lt;p>linear algebra is the study of vector spaces and the structure-preserving maps between them. it is the one branch of mathematics that computers execute natively — every model fit, every graphics frame, every pagerank iteration is matrix arithmetic — and the local model that calculus reduces every smooth problem to.&lt;span class="margin-note" data-note="the jacobian on the multivariable page is exactly this reduction: differentiate, then hand the problem to linear algebra.">
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the plot of this page: spaces, then maps, then the four subspaces every matrix carries, then the two great factorisations — the spectral theorem and the svd (Anton, Howard, 2010).&lt;/p></description></item></channel></rss>