<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Yifu Zhang — Blog</title><description>Notes on numerical analysis, randomized numerical linear algebra, approximation theory, operator learning and related mathematics.</description><link>https://yifuzhang314.github.io/yifu-zhang/</link><language>en-gb</language><item><title>CUR Decomposition: A low-rank approximation with column/row subsets</title><link>https://yifuzhang314.github.io/yifu-zhang/blog/cur-decomposition/</link><guid isPermaLink="true">https://yifuzhang314.github.io/yifu-zhang/blog/cur-decomposition/</guid><description>An intuitive introduction to CUR approximation using actual matrix columns and rows, its practical advantages, and tips for constructing and stabilizing it.</description><pubDate>Fri, 02 Oct 2026 00:00:00 GMT</pubDate><category>Numerical analysis</category><category>Numerical linear algebra</category><category>Low-rank approximation</category></item><item><title>Column Subset Selection: how to choose a small representative subset from millions of data points?</title><link>https://yifuzhang314.github.io/yifu-zhang/blog/column-subset-selection/</link><guid isPermaLink="true">https://yifuzhang314.github.io/yifu-zhang/blog/column-subset-selection/</guid><description>Why a small subset of actual matrix columns can provide an interpretable, structure-preserving low-rank approximation, and how common CSSP algorithms choose them.</description><pubDate>Thu, 03 Sep 2026 00:00:00 GMT</pubDate><category>Numerical analysis</category><category>Numerical linear algebra</category><category>Low-rank approximation</category></item><item><title>How can polynomial interpolation remove noise? (Part I)</title><link>https://yifuzhang314.github.io/yifu-zhang/blog/how-can-polynomial-interpolation-remove-noise-part-i/</link><guid isPermaLink="true">https://yifuzhang314.github.io/yifu-zhang/blog/how-can-polynomial-interpolation-remove-noise-part-i/</guid><description>A simple route from noisy Chebyshev interpolation to a fast, stable polynomial approximation using coefficient truncation and Mallows’ criterion.</description><pubDate>Sun, 23 Aug 2026 00:00:00 GMT</pubDate><category>Numerical analysis</category><category>Approximation theory</category><category>Chebyshev interpolation</category></item></channel></rss>