From fa34a04efa7921a74dff0fcdb957968a937caba9 Mon Sep 17 00:00:00 2001
From: ryc <reid@deepneural.network>
Date: Sat, 2 Mar 2024 23:18:11 -0600
Subject: [PATCH] update

---
 content/notes/nuclear-norm-sdp.md | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/content/notes/nuclear-norm-sdp.md b/content/notes/nuclear-norm-sdp.md
index 2570d92..6ff7259 100644
--- a/content/notes/nuclear-norm-sdp.md
+++ b/content/notes/nuclear-norm-sdp.md
@@ -1,3 +1,12 @@
+#+title: Nuclear Norm via SDP
+#+date: 2023-03-02
+#+category: notes
+#+tags: ml
+
+:PROPERTIES:
+:CUSTOM_ID: matrix-norm
+:END:
+
 # Matrix norms
 
 Given a matrix $X \in \mathbb{R}^{m \times n}$, $\sigma_{i}(X)$ denotes the $i$-th largest singular value of $X$ and is equal to the square root of the $i$-th largest eigenvalue of $XX'$. The rank of $X$, denoted as $\mathrm{rank}(X) = r$ is the number of non-zero singular values.
@@ -162,4 +171,4 @@ This result shows that we can compute the nuclear norm via SDP.
 
 ```
 Recht, Benjamin, Maryam Fazel, and Pablo A. Parrilo. "Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization." _SIAM review_ 52.3 (2010): 471-501.
-```
\ No newline at end of file
+```