From 3e36d933cecfb4f3fc505808c5178d8493604a12 Mon Sep 17 00:00:00 2001
From: Shu <shu.hu@anu.edu.au>
Date: Thu, 6 Oct 2022 23:15:09 +1100
Subject: [PATCH] [svd_intro] fix a small typo

---
 lectures/svd_intro.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/lectures/svd_intro.md b/lectures/svd_intro.md
index c7d394fc2..2a27ab68d 100644
--- a/lectures/svd_intro.md
+++ b/lectures/svd_intro.md
@@ -856,7 +856,7 @@ $$
 X =  U \Sigma  V^T 
 $$ (eq:SVDDMD)
 
-where we remind ourselves that for a **reduced** SVD, $X$ is an $m \times n$ matrix of data, $U$ is an $m \times p$ matrix, $\Sigma$  is a $p \times p$ matrix, and $V is an $n \times p$ matrix.  
+where we remind ourselves that for a **reduced** SVD, $X$ is an $m \times n$ matrix of data, $U$ is an $m \times p$ matrix, $\Sigma$  is a $p \times p$ matrix, and $V$ is an $n \times p$ matrix.  
 
 We can    efficiently  construct the pertinent pseudo-inverse $X^+$
 by recognizing the following string of equalities.