<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Numerical Linear Algebra on Jibon Krishna Joy</title><link>https://jibon-k.com/tags/numerical-linear-algebra/</link><description>Recent content in Numerical Linear Algebra on Jibon Krishna Joy</description><image><title>Jibon Krishna Joy</title><url>https://jibon-k.com/images/og-card.jpg</url><link>https://jibon-k.com/images/og-card.jpg</link></image><generator>Hugo -- 0.166.0</generator><language>en-US</language><copyright>2026 Jibon Krishna Joy</copyright><lastBuildDate>Thu, 18 Dec 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://jibon-k.com/tags/numerical-linear-algebra/index.xml" rel="self" type="application/rss+xml"/><item><title>Randomized Low-Rank Approximation of Kernel Matrices</title><link>https://jibon-k.com/projects/kernel-approximation/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://jibon-k.com/projects/kernel-approximation/</guid><description>Gaussian rSVD, SRHT-rSVD and randQB_EI compared on kernel matrices up to 20,000 by 20,000: accuracy, runtime, memory and the effect of power iteration.</description></item><item><title>Image Compression with Singular Value Decomposition (SVD)</title><link>https://jibon-k.com/blog/svd_compression/</link><pubDate>Thu, 18 Dec 2025 00:00:00 +0000</pubDate><guid>https://jibon-k.com/blog/svd_compression/</guid><description>A computational approach to matrix approximation and image compression using SVD in Python.</description></item></channel></rss>