<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Numerical Methods on SailingDataLakes</title><link>https://sailingdatalakes.com/tags/numerical-methods/</link><description>Recent content in Numerical Methods on SailingDataLakes</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Sat, 04 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://sailingdatalakes.com/tags/numerical-methods/index.xml" rel="self" type="application/rss+xml"/><item><title>Root-Finding: Newton, Halley, and Secant Methods</title><link>https://sailingdatalakes.com/posts/root-finding/</link><pubDate>Sat, 04 Jul 2026 00:00:00 +0000</pubDate><guid>https://sailingdatalakes.com/posts/root-finding/</guid><description>Purpose Link to heading We&amp;rsquo;ve now solved for model parameters directly, with linear regression&amp;rsquo;s closed-form OLS solution, and iteratively, with gradient descent. Both problems boiled down to finding where a derivative equals zero. The more general version of that problem — finding where some function $f$ itself equals zero, with no derivative in sight — shows up everywhere: pricing a bond to match a target yield, finding the break-even point of a nonlinear cost curve, or solving an equilibrium condition that has no closed form.</description></item></channel></rss>