<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Convexity on Arshad Siddiqui</title><link>https://arshadhs.github.io/tags/convexity/</link><description>Recent content in Convexity on Arshad Siddiqui</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 29 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://arshadhs.github.io/tags/convexity/index.xml" rel="self" type="application/rss+xml"/><item><title>Convex Combination</title><link>https://arshadhs.github.io/docs/ai/maths/010-linear-algebra/01-linear-systems/convex/</link><pubDate>Thu, 29 Jan 2026 00:00:00 +0000</pubDate><guid>https://arshadhs.github.io/docs/ai/maths/010-linear-algebra/01-linear-systems/convex/</guid><description>&lt;h1 id="convex-combination-of-two-points">
 Convex Combination of Two Points
 
 &lt;a class="anchor" href="#convex-combination-of-two-points">#&lt;/a>
 
&lt;/h1>
&lt;p>A &lt;strong>convex combination&lt;/strong> describes how to form a point between two points using weighted averages.&lt;/p>
&lt;p>It is a fundamental building block in several advanced fields:&lt;/p>
&lt;ul>
&lt;li>&lt;strong>Linear Algebra &amp;amp; Geometry&lt;/strong>&lt;/li>
&lt;li>&lt;strong>Optimization Theory&lt;/strong>&lt;/li>
&lt;li>&lt;strong>Machine Learning&lt;/strong> (Specifically in SVMs, clustering, and data interpolation)&lt;/li>
&lt;/ul>
&lt;hr>
&lt;p>Given two points (or vectors) $\mathbf{x}_1, \mathbf{x}_2 \in \mathbb{R}^n$, a convex combination of these points is defined as:&lt;/p>
$$\mathbf{x} = \lambda \mathbf{x}_1 + (1 - \lambda)\mathbf{x}_2$$&lt;p>&lt;strong>Where:&lt;/strong>&lt;/p></description></item></channel></rss>