From 3e96e16c8e9cc09a51d12bd898faece005095e0d Mon Sep 17 00:00:00 2001 From: Thomas Dehaeze Date: Mon, 21 Sep 2020 17:02:44 +0200 Subject: [PATCH] Update Content - 2020-09-21 --- content/zettels/singular_value_decomposition.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zettels/singular_value_decomposition.md b/content/zettels/singular_value_decomposition.md index 323cc8f..46f769d 100644 --- a/content/zettels/singular_value_decomposition.md +++ b/content/zettels/singular_value_decomposition.md @@ -45,7 +45,7 @@ We define \\(u\_1 = \bar{u}\\), \\(v\_1 = \bar{v}\\), \\(u\_k=\ubar{u}\\) and \\ ## SVD to pseudo inverse rectangular matrices {#svd-to-pseudo-inverse-rectangular-matrices} -This is taken from [Preumont's book](preumont18_vibrat_contr_activ_struc_fourt_edition.md). +This is taken from [Singular Value Decomposition]({{< relref "preumont18_vibrat_contr_activ_struc_fourt_edition" >}}). The **Singular Value Decomposition** (SVD) is a generalization of the eigenvalue decomposition of a rectangular matrix: \\[ J = U \Sigma V^T = \sum\_{i=1}^r \sigma\_i u\_i v\_i^T \\]