-
diff --git a/doc/pub/week36/html/._week36-bs008.html b/doc/pub/week36/html/._week36-bs008.html index 1e7df6297..e7d25780e 100644 --- a/doc/pub/week36/html/._week36-bs008.html +++ b/doc/pub/week36/html/._week36-bs008.html @@ -284,12 +284,12 @@ It is also called the the Moore-Penrose Inverse after two independent discoverer It is used for the calculation of the inverse for singular or near singular matrices and for rectangular matrices.
-Using the SVD we can obtain the pseudoinverse of a matrix \( \boldsymbol{A} \) (labeled here as \( \boldsymbol{A}_{\mathrm{PI}} \) +Using the SVD we can obtain the pseudoinverse of a matrix \( \boldsymbol{A} \) (labeled here as \( \boldsymbol{A}_{\mathrm{PI}} \)) $$ \boldsymbol{A}_{\mathrm{PI}}= \boldsymbol{V}\boldsymbol{D}_{\mathrm{PI}}\boldsymbol{U}^T, $$ -where \( \boldsymbol{D}_{\mathrm{PI}} \) can be calculated by creating a diagonal matrix from \( \boldsymbol{Sigma} \) where we only keep the singular values (the non-zero values). The following code computes the pseudoinvers of the matrix based on the SVD. +where \( \boldsymbol{D}_{\mathrm{PI}} \) can be calculated by creating a diagonal matrix from \( \boldsymbol{\Sigma} \) where we only keep the singular values (the non-zero values). The following code computes the pseudoinvers of the matrix based on the SVD.
diff --git a/doc/pub/week36/html/week36-bs.html b/doc/pub/week36/html/week36-bs.html index 11ff82d8c..869b857dc 100644 --- a/doc/pub/week36/html/week36-bs.html +++ b/doc/pub/week36/html/week36-bs.html @@ -289,7 +289,7 @@ MathJax.Hub.Config({
-
diff --git a/doc/pub/week36/html/week36-reveal.html b/doc/pub/week36/html/week36-reveal.html index e7ceb1224..e8ff537a8 100644 --- a/doc/pub/week36/html/week36-reveal.html +++ b/doc/pub/week36/html/week36-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({
-
@@ -393,14 +393,14 @@ It is also called the the Moore-Penrose Inverse after two independent discoverer It is used for the calculation of the inverse for singular or near singular matrices and for rectangular matrices.
-Using the SVD we can obtain the pseudoinverse of a matrix \( \boldsymbol{A} \) (labeled here as \( \boldsymbol{A}_{\mathrm{PI}} \) +Using the SVD we can obtain the pseudoinverse of a matrix \( \boldsymbol{A} \) (labeled here as \( \boldsymbol{A}_{\mathrm{PI}} \))
$$
\boldsymbol{A}_{\mathrm{PI}}= \boldsymbol{V}\boldsymbol{D}_{\mathrm{PI}}\boldsymbol{U}^T,
$$
-where \( \boldsymbol{D}_{\mathrm{PI}} \) can be calculated by creating a diagonal matrix from \( \boldsymbol{Sigma} \) where we only keep the singular values (the non-zero values). The following code computes the pseudoinvers of the matrix based on the SVD.
+where \( \boldsymbol{D}_{\mathrm{PI}} \) can be calculated by creating a diagonal matrix from \( \boldsymbol{\Sigma} \) where we only keep the singular values (the non-zero values). The following code computes the pseudoinvers of the matrix based on the SVD.
diff --git a/doc/pub/week36/html/week36-solarized.html b/doc/pub/week36/html/week36-solarized.html index 4c72ddc80..eaeefc71d 100644 --- a/doc/pub/week36/html/week36-solarized.html +++ b/doc/pub/week36/html/week36-solarized.html @@ -229,7 +229,7 @@ MathJax.Hub.Config({
-
@@ -443,12 +443,12 @@ It is also called the the Moore-Penrose Inverse after two independent discoverer
It is used for the calculation of the inverse for singular or near singular matrices and for rectangular matrices.
-Using the SVD we can obtain the pseudoinverse of a matrix \( \boldsymbol{A} \) (labeled here as \( \boldsymbol{A}_{\mathrm{PI}} \) +Using the SVD we can obtain the pseudoinverse of a matrix \( \boldsymbol{A} \) (labeled here as \( \boldsymbol{A}_{\mathrm{PI}} \)) $$ \boldsymbol{A}_{\mathrm{PI}}= \boldsymbol{V}\boldsymbol{D}_{\mathrm{PI}}\boldsymbol{U}^T, $$ -where \( \boldsymbol{D}_{\mathrm{PI}} \) can be calculated by creating a diagonal matrix from \( \boldsymbol{Sigma} \) where we only keep the singular values (the non-zero values). The following code computes the pseudoinvers of the matrix based on the SVD. +where \( \boldsymbol{D}_{\mathrm{PI}} \) can be calculated by creating a diagonal matrix from \( \boldsymbol{\Sigma} \) where we only keep the singular values (the non-zero values). The following code computes the pseudoinvers of the matrix based on the SVD.
diff --git a/doc/pub/week36/html/week36.html b/doc/pub/week36/html/week36.html index bff48eb27..933870fce 100644 --- a/doc/pub/week36/html/week36.html +++ b/doc/pub/week36/html/week36.html @@ -234,7 +234,7 @@ MathJax.Hub.Config({
-
@@ -448,12 +448,12 @@ It is also called the the Moore-Penrose Inverse after two independent discoverer
It is used for the calculation of the inverse for singular or near singular matrices and for rectangular matrices.
-Using the SVD we can obtain the pseudoinverse of a matrix \( \boldsymbol{A} \) (labeled here as \( \boldsymbol{A}_{\mathrm{PI}} \) +Using the SVD we can obtain the pseudoinverse of a matrix \( \boldsymbol{A} \) (labeled here as \( \boldsymbol{A}_{\mathrm{PI}} \)) $$ \boldsymbol{A}_{\mathrm{PI}}= \boldsymbol{V}\boldsymbol{D}_{\mathrm{PI}}\boldsymbol{U}^T, $$ -where \( \boldsymbol{D}_{\mathrm{PI}} \) can be calculated by creating a diagonal matrix from \( \boldsymbol{Sigma} \) where we only keep the singular values (the non-zero values). The following code computes the pseudoinvers of the matrix based on the SVD. +where \( \boldsymbol{D}_{\mathrm{PI}} \) can be calculated by creating a diagonal matrix from \( \boldsymbol{\Sigma} \) where we only keep the singular values (the non-zero values). The following code computes the pseudoinvers of the matrix based on the SVD.
diff --git a/doc/pub/week36/ipynb/ipynb-week36-src.tar.gz b/doc/pub/week36/ipynb/ipynb-week36-src.tar.gz index c492f019c..c22e35956 100644 Binary files a/doc/pub/week36/ipynb/ipynb-week36-src.tar.gz and b/doc/pub/week36/ipynb/ipynb-week36-src.tar.gz differ diff --git a/doc/pub/week36/ipynb/week36.ipynb b/doc/pub/week36/ipynb/week36.ipynb index 28ddb3cf6..7812628d6 100644 --- a/doc/pub/week36/ipynb/week36.ipynb +++ b/doc/pub/week36/ipynb/week36.ipynb @@ -10,7 +10,7 @@ " \n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", "\n", - "Date: **Sep 12, 2021**\n", + "Date: **Sep 16, 2021**\n", "\n", "Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", @@ -334,7 +334,7 @@ "It is also called the the Moore-Penrose Inverse after two independent discoverers of the method or the Generalized Inverse.\n", "It is used for the calculation of the inverse for singular or near singular matrices and for rectangular matrices.\n", "\n", - "Using the SVD we can obtain the pseudoinverse of a matrix $\\boldsymbol{A}$ (labeled here as $\\boldsymbol{A}_{\\mathrm{PI}}$" + "Using the SVD we can obtain the pseudoinverse of a matrix $\\boldsymbol{A}$ (labeled here as $\\boldsymbol{A}_{\\mathrm{PI}}$)" ] }, { @@ -350,7 +350,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "where $\\boldsymbol{D}_{\\mathrm{PI}}$ can be calculated by creating a diagonal matrix from $\\boldsymbol{Sigma}$ where we only keep the singular values (the non-zero values). The following code computes the pseudoinvers of the matrix based on the SVD." + "where $\\boldsymbol{D}_{\\mathrm{PI}}$ can be calculated by creating a diagonal matrix from $\\boldsymbol{\\Sigma}$ where we only keep the singular values (the non-zero values). The following code computes the pseudoinvers of the matrix based on the SVD." ] }, { diff --git a/doc/src/week36/week36.do.txt b/doc/src/week36/week36.do.txt index 450a493d2..1c4bf8556 100644 --- a/doc/src/week36/week36.do.txt +++ b/doc/src/week36/week36.do.txt @@ -200,13 +200,13 @@ rectangular matrices where the number of rows and columns are not equal. It is also called the the Moore-Penrose Inverse after two independent discoverers of the method or the Generalized Inverse. It is used for the calculation of the inverse for singular or near singular matrices and for rectangular matrices. -Using the SVD we can obtain the pseudoinverse of a matrix $\bm{A}$ (labeled here as $\bm{A}_{\mathrm{PI}}$ +Using the SVD we can obtain the pseudoinverse of a matrix $\bm{A}$ (labeled here as $\bm{A}_{\mathrm{PI}}$) !bt \[ \bm{A}_{\mathrm{PI}}= \bm{V}\bm{D}_{\mathrm{PI}}\bm{U}^T, \] !et -where $\bm{D}_{\mathrm{PI}}$ can be calculated by creating a diagonal matrix from $\bm{Sigma}$ where we only keep the singular values (the non-zero values). The following code computes the pseudoinvers of the matrix based on the SVD. +where $\bm{D}_{\mathrm{PI}}$ can be calculated by creating a diagonal matrix from $\bm{\Sigma}$ where we only keep the singular values (the non-zero values). The following code computes the pseudoinvers of the matrix based on the SVD. !bc pycod