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small typos in week36

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Morten Hjorth-Jensen
2021-09-16 05:37:28 +02:00
parent c556620351
commit b7307fb7aa
9 changed files with 18 additions and 18 deletions
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doc/pub/week36/html/._week36-bs000.html
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@@ -289,7 +289,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 12, 2021</h4></center> <!-- date -->
<center><h4>Sep 16, 2021</h4></center> <!-- date -->
<br>
<p>
doc/pub/week36/html/._week36-bs008.html
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@@ -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.
<p>
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.
<p>
doc/pub/week36/html/week36-bs.html
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@@ -289,7 +289,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 12, 2021</h4></center> <!-- date -->
<center><h4>Sep 16, 2021</h4></center> <!-- date -->
<br>
<p>
doc/pub/week36/html/week36-reveal.html
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@@ -148,7 +148,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>&nbsp;<br>
<center><h4>Sep 12, 2021</h4></center> <!-- date -->
<center><h4>Sep 16, 2021</h4></center> <!-- date -->
<br>
<p>
@@ -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.
<p>
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 \( \boldsymbol{A}_{\mathrm{PI}} \))
<p>&nbsp;<br>
$$
\boldsymbol{A}_{\mathrm{PI}}= \boldsymbol{V}\boldsymbol{D}_{\mathrm{PI}}\boldsymbol{U}^T,
$$
<p>&nbsp;<br>
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.
<p>
doc/pub/week36/html/week36-solarized.html
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@@ -229,7 +229,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 12, 2021</h4></center> <!-- date -->
<center><h4>Sep 16, 2021</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -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.
<p>
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.
<p>
doc/pub/week36/html/week36.html
+3 -3
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@@ -234,7 +234,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 12, 2021</h4></center> <!-- date -->
<center><h4>Sep 16, 2021</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -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.
<p>
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.
<p>
doc/pub/week36/ipynb/ipynb-week36-src.tar.gz
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doc/pub/week36/ipynb/week36.ipynb
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@@ -10,7 +10,7 @@
"<!-- Author: --> \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."
]
},
{
doc/src/week36/week36.do.txt
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@@ -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