From 40fe4a0d90ea57086d51d16a1596a43c826da991 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Thu, 17 Nov 2022 11:35:59 +0100
Subject: [PATCH] problems with quotations in python code

---
 doc/pub/week46/html/._week46-bs029.html      |  12 +-
 doc/pub/week46/html/week46-reveal.html       |  14 +-
 doc/pub/week46/html/week46-solarized.html    |  14 +-
 doc/pub/week46/html/week46.html              |  14 +-
 doc/pub/week46/ipynb/ipynb-week46-src.tar.gz | Bin 192 -> 192 bytes
 doc/pub/week46/ipynb/week46.ipynb            | 360 +++++++++----------
 doc/src/week46/week46.do.txt                 |  13 +-
 7 files changed, 209 insertions(+), 218 deletions(-)

diff --git a/doc/pub/week46/html/._week46-bs029.html b/doc/pub/week46/html/._week46-bs029.html
index 0e5dff381..76e5ee1d1 100644
--- a/doc/pub/week46/html/._week46-bs029.html
+++ b/doc/pub/week46/html/._week46-bs029.html
@@ -235,15 +235,15 @@ The following code solves the equations for us
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">cvxopt</span> <span style="color: #008000; font-weight: bold">import</span> matrix
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">cvxopt</span> <span style="color: #008000; font-weight: bold">import</span> solvers
-P <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>diag([<span style="color: #666666">1</span>,<span style="color: #666666">0</span>]), tc<span style="color: #666666">=</span>’d’)
-q <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([<span style="color: #666666">3</span>,<span style="color: #666666">4</span>]), tc<span style="color: #666666">=</span>’d’)
-G <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([[<span style="color: #666666">-1</span>,<span style="color: #666666">0</span>],[<span style="color: #666666">0</span>,<span style="color: #666666">-1</span>],[<span style="color: #666666">-1</span>,<span style="color: #666666">-3</span>],[<span style="color: #666666">2</span>,<span style="color: #666666">5</span>],[<span style="color: #666666">3</span>,<span style="color: #666666">4</span>]]), tc<span style="color: #666666">=</span>’d’)
-h <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([<span style="color: #666666">0</span>,<span style="color: #666666">0</span>,<span style="color: #666666">-15</span>,<span style="color: #666666">100</span>,<span style="color: #666666">80</span>]), tc<span style="color: #666666">=</span>’d’)
+P <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>diag([<span style="color: #666666">1</span>,<span style="color: #666666">0</span>]), tc<span style="color: #666666">=</span><span style="color: #BA2121">&#39;d&#39;</span>)
+q <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([<span style="color: #666666">3</span>,<span style="color: #666666">4</span>]), tc<span style="color: #666666">=</span><span style="color: #BA2121">&#39;d&#39;</span>)
+G <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([[<span style="color: #666666">-1</span>,<span style="color: #666666">0</span>],[<span style="color: #666666">0</span>,<span style="color: #666666">-1</span>],[<span style="color: #666666">-1</span>,<span style="color: #666666">-3</span>],[<span style="color: #666666">2</span>,<span style="color: #666666">5</span>],[<span style="color: #666666">3</span>,<span style="color: #666666">4</span>]]), tc<span style="color: #666666">=</span><span style="color: #BA2121">&#39;d&#39;</span>)
+h <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([<span style="color: #666666">0</span>,<span style="color: #666666">0</span>,<span style="color: #666666">-15</span>,<span style="color: #666666">100</span>,<span style="color: #666666">80</span>]), tc<span style="color: #666666">=</span><span style="color: #BA2121">&#39;d&#39;</span>)
 <span style="color: #408080; font-style: italic"># Construct the QP, invoke solver</span>
 sol <span style="color: #666666">=</span> solvers<span style="color: #666666">.</span>qp(P,q,G,h)
 <span style="color: #408080; font-style: italic"># Extract optimal value and solution</span>
-sol[’x’] 
-sol[’primal objective’]
+sol[<span style="color: #BA2121">&#39;x&#39;</span>] 
+sol[<span style="color: #BA2121">&#39;primal objective&#39;</span>]
 </pre>
 </div>
       </div>
diff --git a/doc/pub/week46/html/week46-reveal.html b/doc/pub/week46/html/week46-reveal.html
index 5a4ad358e..35f145a29 100644
--- a/doc/pub/week46/html/week46-reveal.html
+++ b/doc/pub/week46/html/week46-reveal.html
@@ -1584,15 +1584,15 @@ The following code solves the equations for us
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">cvxopt</span> <span style="color: #8B008B; font-weight: bold">import</span> matrix
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">cvxopt</span> <span style="color: #8B008B; font-weight: bold">import</span> solvers
-P = matrix(numpy.diag([<span style="color: #B452CD">1</span>,<span style="color: #B452CD">0</span>]), tc=<span style="color: #a61717; background-color: #e3d2d2">’</span>d<span style="color: #a61717; background-color: #e3d2d2">’</span>)
-q = matrix(numpy.array([<span style="color: #B452CD">3</span>,<span style="color: #B452CD">4</span>]), tc=<span style="color: #a61717; background-color: #e3d2d2">’</span>d<span style="color: #a61717; background-color: #e3d2d2">’</span>)
-G = matrix(numpy.array([[-<span style="color: #B452CD">1</span>,<span style="color: #B452CD">0</span>],[<span style="color: #B452CD">0</span>,-<span style="color: #B452CD">1</span>],[-<span style="color: #B452CD">1</span>,-<span style="color: #B452CD">3</span>],[<span style="color: #B452CD">2</span>,<span style="color: #B452CD">5</span>],[<span style="color: #B452CD">3</span>,<span style="color: #B452CD">4</span>]]), tc=<span style="color: #a61717; background-color: #e3d2d2">’</span>d<span style="color: #a61717; background-color: #e3d2d2">’</span>)
-h = matrix(numpy.array([<span style="color: #B452CD">0</span>,<span style="color: #B452CD">0</span>,-<span style="color: #B452CD">15</span>,<span style="color: #B452CD">100</span>,<span style="color: #B452CD">80</span>]), tc=<span style="color: #a61717; background-color: #e3d2d2">’</span>d<span style="color: #a61717; background-color: #e3d2d2">’</span>)
+P = matrix(numpy.diag([<span style="color: #B452CD">1</span>,<span style="color: #B452CD">0</span>]), tc=<span style="color: #CD5555">&#39;d&#39;</span>)
+q = matrix(numpy.array([<span style="color: #B452CD">3</span>,<span style="color: #B452CD">4</span>]), tc=<span style="color: #CD5555">&#39;d&#39;</span>)
+G = matrix(numpy.array([[-<span style="color: #B452CD">1</span>,<span style="color: #B452CD">0</span>],[<span style="color: #B452CD">0</span>,-<span style="color: #B452CD">1</span>],[-<span style="color: #B452CD">1</span>,-<span style="color: #B452CD">3</span>],[<span style="color: #B452CD">2</span>,<span style="color: #B452CD">5</span>],[<span style="color: #B452CD">3</span>,<span style="color: #B452CD">4</span>]]), tc=<span style="color: #CD5555">&#39;d&#39;</span>)
+h = matrix(numpy.array([<span style="color: #B452CD">0</span>,<span style="color: #B452CD">0</span>,-<span style="color: #B452CD">15</span>,<span style="color: #B452CD">100</span>,<span style="color: #B452CD">80</span>]), tc=<span style="color: #CD5555">&#39;d&#39;</span>)
 <span style="color: #228B22"># Construct the QP, invoke solver</span>
 sol = solvers.qp(P,q,G,h)
 <span style="color: #228B22"># Extract optimal value and solution</span>
-sol[<span style="color: #a61717; background-color: #e3d2d2">’</span>x<span style="color: #a61717; background-color: #e3d2d2">’</span>] 
-sol[<span style="color: #a61717; background-color: #e3d2d2">’</span>primal objective<span style="color: #a61717; background-color: #e3d2d2">’</span>]
+sol[<span style="color: #CD5555">&#39;x&#39;</span>] 
+sol[<span style="color: #CD5555">&#39;primal objective&#39;</span>]
 </pre>
 </div>
       </div>
@@ -1628,8 +1628,6 @@ $$
 \( \boldsymbol{y}=[y_1,y_2,\dots,y_n] \). 
 With  the slack constants this leads to the additional constraint \( 0\leq \lambda_i \leq C \).
 </p>
-
-<b>code will be added</b>
 </section>
 
 
diff --git a/doc/pub/week46/html/week46-solarized.html b/doc/pub/week46/html/week46-solarized.html
index af76d1937..6eb3dc309 100644
--- a/doc/pub/week46/html/week46-solarized.html
+++ b/doc/pub/week46/html/week46-solarized.html
@@ -1399,15 +1399,15 @@ The following code solves the equations for us
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">numpy</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">cvxopt</span> <span style="color: #8B008B; font-weight: bold">import</span> matrix
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">cvxopt</span> <span style="color: #8B008B; font-weight: bold">import</span> solvers
-P = matrix(numpy.diag([<span style="color: #B452CD">1</span>,<span style="color: #B452CD">0</span>]), tc=<span style="color: #a61717; background-color: #e3d2d2">’</span>d<span style="color: #a61717; background-color: #e3d2d2">’</span>)
-q = matrix(numpy.array([<span style="color: #B452CD">3</span>,<span style="color: #B452CD">4</span>]), tc=<span style="color: #a61717; background-color: #e3d2d2">’</span>d<span style="color: #a61717; background-color: #e3d2d2">’</span>)
-G = matrix(numpy.array([[-<span style="color: #B452CD">1</span>,<span style="color: #B452CD">0</span>],[<span style="color: #B452CD">0</span>,-<span style="color: #B452CD">1</span>],[-<span style="color: #B452CD">1</span>,-<span style="color: #B452CD">3</span>],[<span style="color: #B452CD">2</span>,<span style="color: #B452CD">5</span>],[<span style="color: #B452CD">3</span>,<span style="color: #B452CD">4</span>]]), tc=<span style="color: #a61717; background-color: #e3d2d2">’</span>d<span style="color: #a61717; background-color: #e3d2d2">’</span>)
-h = matrix(numpy.array([<span style="color: #B452CD">0</span>,<span style="color: #B452CD">0</span>,-<span style="color: #B452CD">15</span>,<span style="color: #B452CD">100</span>,<span style="color: #B452CD">80</span>]), tc=<span style="color: #a61717; background-color: #e3d2d2">’</span>d<span style="color: #a61717; background-color: #e3d2d2">’</span>)
+P = matrix(numpy.diag([<span style="color: #B452CD">1</span>,<span style="color: #B452CD">0</span>]), tc=<span style="color: #CD5555">&#39;d&#39;</span>)
+q = matrix(numpy.array([<span style="color: #B452CD">3</span>,<span style="color: #B452CD">4</span>]), tc=<span style="color: #CD5555">&#39;d&#39;</span>)
+G = matrix(numpy.array([[-<span style="color: #B452CD">1</span>,<span style="color: #B452CD">0</span>],[<span style="color: #B452CD">0</span>,-<span style="color: #B452CD">1</span>],[-<span style="color: #B452CD">1</span>,-<span style="color: #B452CD">3</span>],[<span style="color: #B452CD">2</span>,<span style="color: #B452CD">5</span>],[<span style="color: #B452CD">3</span>,<span style="color: #B452CD">4</span>]]), tc=<span style="color: #CD5555">&#39;d&#39;</span>)
+h = matrix(numpy.array([<span style="color: #B452CD">0</span>,<span style="color: #B452CD">0</span>,-<span style="color: #B452CD">15</span>,<span style="color: #B452CD">100</span>,<span style="color: #B452CD">80</span>]), tc=<span style="color: #CD5555">&#39;d&#39;</span>)
 <span style="color: #228B22"># Construct the QP, invoke solver</span>
 sol = solvers.qp(P,q,G,h)
 <span style="color: #228B22"># Extract optimal value and solution</span>
-sol[<span style="color: #a61717; background-color: #e3d2d2">’</span>x<span style="color: #a61717; background-color: #e3d2d2">’</span>] 
-sol[<span style="color: #a61717; background-color: #e3d2d2">’</span>primal objective<span style="color: #a61717; background-color: #e3d2d2">’</span>]
+sol[<span style="color: #CD5555">&#39;x&#39;</span>] 
+sol[<span style="color: #CD5555">&#39;primal objective&#39;</span>]
 </pre>
 </div>
       </div>
@@ -1442,8 +1442,6 @@ $$
 With  the slack constants this leads to the additional constraint \( 0\leq \lambda_i \leq C \).
 </p>
 
-<b>code will be added</b>
-
 <!-- ------------------- end of main content --------------- -->
 <center style="font-size:80%">
 <!-- copyright --> &copy; 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
diff --git a/doc/pub/week46/html/week46.html b/doc/pub/week46/html/week46.html
index 1bde18e5d..6642e43c7 100644
--- a/doc/pub/week46/html/week46.html
+++ b/doc/pub/week46/html/week46.html
@@ -1476,15 +1476,15 @@ The following code solves the equations for us
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">cvxopt</span> <span style="color: #008000; font-weight: bold">import</span> matrix
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">cvxopt</span> <span style="color: #008000; font-weight: bold">import</span> solvers
-P <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>diag([<span style="color: #666666">1</span>,<span style="color: #666666">0</span>]), tc<span style="color: #666666">=</span>’d’)
-q <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([<span style="color: #666666">3</span>,<span style="color: #666666">4</span>]), tc<span style="color: #666666">=</span>’d’)
-G <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([[<span style="color: #666666">-1</span>,<span style="color: #666666">0</span>],[<span style="color: #666666">0</span>,<span style="color: #666666">-1</span>],[<span style="color: #666666">-1</span>,<span style="color: #666666">-3</span>],[<span style="color: #666666">2</span>,<span style="color: #666666">5</span>],[<span style="color: #666666">3</span>,<span style="color: #666666">4</span>]]), tc<span style="color: #666666">=</span>’d’)
-h <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([<span style="color: #666666">0</span>,<span style="color: #666666">0</span>,<span style="color: #666666">-15</span>,<span style="color: #666666">100</span>,<span style="color: #666666">80</span>]), tc<span style="color: #666666">=</span>’d’)
+P <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>diag([<span style="color: #666666">1</span>,<span style="color: #666666">0</span>]), tc<span style="color: #666666">=</span><span style="color: #BA2121">&#39;d&#39;</span>)
+q <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([<span style="color: #666666">3</span>,<span style="color: #666666">4</span>]), tc<span style="color: #666666">=</span><span style="color: #BA2121">&#39;d&#39;</span>)
+G <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([[<span style="color: #666666">-1</span>,<span style="color: #666666">0</span>],[<span style="color: #666666">0</span>,<span style="color: #666666">-1</span>],[<span style="color: #666666">-1</span>,<span style="color: #666666">-3</span>],[<span style="color: #666666">2</span>,<span style="color: #666666">5</span>],[<span style="color: #666666">3</span>,<span style="color: #666666">4</span>]]), tc<span style="color: #666666">=</span><span style="color: #BA2121">&#39;d&#39;</span>)
+h <span style="color: #666666">=</span> matrix(numpy<span style="color: #666666">.</span>array([<span style="color: #666666">0</span>,<span style="color: #666666">0</span>,<span style="color: #666666">-15</span>,<span style="color: #666666">100</span>,<span style="color: #666666">80</span>]), tc<span style="color: #666666">=</span><span style="color: #BA2121">&#39;d&#39;</span>)
 <span style="color: #408080; font-style: italic"># Construct the QP, invoke solver</span>
 sol <span style="color: #666666">=</span> solvers<span style="color: #666666">.</span>qp(P,q,G,h)
 <span style="color: #408080; font-style: italic"># Extract optimal value and solution</span>
-sol[’x’] 
-sol[’primal objective’]
+sol[<span style="color: #BA2121">&#39;x&#39;</span>] 
+sol[<span style="color: #BA2121">&#39;primal objective&#39;</span>]
 </pre>
 </div>
       </div>
@@ -1519,8 +1519,6 @@ $$
 With  the slack constants this leads to the additional constraint \( 0\leq \lambda_i \leq C \).
 </p>
 
-<b>code will be added</b>
-
 <!-- ------------------- end of main content --------------- -->
 <center style="font-size:80%">
 <!-- copyright --> &copy; 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
diff --git a/doc/pub/week46/ipynb/ipynb-week46-src.tar.gz b/doc/pub/week46/ipynb/ipynb-week46-src.tar.gz
index 71b644ccaaac060fd86ec1bcb02cb9ab7faa8180..846111d950fb3972f89438ccaf9fd63601dfe72e 100644
GIT binary patch
delta 136
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qKkS)bfoJ}SV<jzY_r0sM0;L`1TGwzxta%cVkSGXr*+#?w2mk<HNIJg&

delta 136
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qANI_zz%&2Ev62?H``%Srfzl3ht!ua;)=3(XkSGY;Z+t-j2mk<!***pU

diff --git a/doc/pub/week46/ipynb/week46.ipynb b/doc/pub/week46/ipynb/week46.ipynb
index ab04590da..0830feb18 100644
--- a/doc/pub/week46/ipynb/week46.ipynb
+++ b/doc/pub/week46/ipynb/week46.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d33120af",
+   "id": "c729ff05",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8bc720cc",
+   "id": "ea7ce43a",
    "metadata": {
     "editable": true
    },
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ade03988",
+   "id": "2f795544",
    "metadata": {
     "editable": true
    },
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7dd29a4a",
+   "id": "4ca2acda",
    "metadata": {
     "editable": true
    },
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0684f07",
+   "id": "cddfb56d",
    "metadata": {
     "editable": true
    },
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18cb0dca",
+   "id": "38b91750",
    "metadata": {
     "editable": true
    },
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d85355fe",
+   "id": "05d68973",
    "metadata": {
     "editable": true
    },
@@ -163,7 +163,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "41a222bc",
+   "id": "c9d58ad1",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -242,7 +242,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32651474",
+   "id": "d07c7ddc",
    "metadata": {
     "editable": true
    },
@@ -262,7 +262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebe40ef1",
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@@ -274,7 +274,7 @@
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@@ -287,7 +287,7 @@
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@@ -299,7 +299,7 @@
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@@ -312,7 +312,7 @@
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@@ -324,7 +324,7 @@
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@@ -336,7 +336,7 @@
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@@ -348,7 +348,7 @@
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@@ -358,7 +358,7 @@
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@@ -370,7 +370,7 @@
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@@ -381,7 +381,7 @@
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@@ -393,7 +393,7 @@
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@@ -406,7 +406,7 @@
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@@ -418,7 +418,7 @@
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@@ -428,7 +428,7 @@
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@@ -457,7 +457,7 @@
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@@ -469,7 +469,7 @@
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@@ -481,7 +481,7 @@
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@@ -495,7 +495,7 @@
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@@ -507,7 +507,7 @@
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@@ -521,7 +521,7 @@
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@@ -533,7 +533,7 @@
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@@ -543,7 +543,7 @@
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@@ -555,7 +555,7 @@
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@@ -565,7 +565,7 @@
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@@ -577,7 +577,7 @@
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@@ -589,7 +589,7 @@
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@@ -601,7 +601,7 @@
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@@ -611,7 +611,7 @@
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@@ -623,7 +623,7 @@
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@@ -633,7 +633,7 @@
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@@ -647,7 +647,7 @@
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@@ -666,7 +666,7 @@
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@@ -682,7 +682,7 @@
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@@ -694,7 +694,7 @@
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@@ -706,7 +706,7 @@
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@@ -718,7 +718,7 @@
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@@ -728,7 +728,7 @@
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@@ -740,7 +740,7 @@
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@@ -751,7 +751,7 @@
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@@ -763,7 +763,7 @@
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@@ -776,7 +776,7 @@
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@@ -789,7 +789,7 @@
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@@ -801,7 +801,7 @@
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@@ -811,7 +811,7 @@
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@@ -823,7 +823,7 @@
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@@ -833,7 +833,7 @@
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@@ -845,7 +845,7 @@
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@@ -862,7 +862,7 @@
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@@ -874,7 +874,7 @@
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@@ -884,7 +884,7 @@
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@@ -896,7 +896,7 @@
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@@ -906,7 +906,7 @@
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@@ -918,7 +918,7 @@
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@@ -931,7 +931,7 @@
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@@ -943,7 +943,7 @@
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@@ -955,7 +955,7 @@
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@@ -965,7 +965,7 @@
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@@ -979,7 +979,7 @@
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@@ -989,7 +989,7 @@
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@@ -1001,7 +1001,7 @@
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@@ -1011,7 +1011,7 @@
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@@ -1023,7 +1023,7 @@
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@@ -1033,7 +1033,7 @@
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@@ -1045,7 +1045,7 @@
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@@ -1059,7 +1059,7 @@
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@@ -1071,7 +1071,7 @@
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@@ -1082,7 +1082,7 @@
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@@ -1094,7 +1094,7 @@
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@@ -1106,7 +1106,7 @@
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@@ -1118,7 +1118,7 @@
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@@ -1128,7 +1128,7 @@
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@@ -1140,7 +1140,7 @@
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@@ -1150,7 +1150,7 @@
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@@ -1162,7 +1162,7 @@
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@@ -1173,7 +1173,7 @@
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@@ -1185,7 +1185,7 @@
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@@ -1199,7 +1199,7 @@
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@@ -1211,7 +1211,7 @@
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@@ -1223,7 +1223,7 @@
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@@ -1233,7 +1233,7 @@
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@@ -1250,7 +1250,7 @@
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@@ -1261,7 +1261,7 @@
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@@ -1274,7 +1274,7 @@
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@@ -1286,7 +1286,7 @@
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@@ -1296,7 +1296,7 @@
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@@ -1308,7 +1308,7 @@
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@@ -1318,7 +1318,7 @@
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@@ -1330,7 +1330,7 @@
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@@ -1340,7 +1340,7 @@
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@@ -1352,7 +1352,7 @@
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@@ -1362,7 +1362,7 @@
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@@ -1374,7 +1374,7 @@
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@@ -1384,7 +1384,7 @@
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@@ -1404,7 +1404,7 @@
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@@ -1416,7 +1416,7 @@
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@@ -1426,7 +1426,7 @@
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@@ -1438,7 +1438,7 @@
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@@ -1454,7 +1454,7 @@
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@@ -1466,7 +1466,7 @@
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@@ -1478,7 +1478,7 @@
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@@ -1488,7 +1488,7 @@
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@@ -1500,7 +1500,7 @@
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@@ -1512,7 +1512,7 @@
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+   "id": "f5a1a4ef",
    "metadata": {
     "editable": true
    },
@@ -1524,7 +1524,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57cb2be5",
+   "id": "2ebd58cd",
    "metadata": {
     "editable": true
    },
@@ -1534,7 +1534,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e5a1dc8",
+   "id": "36be3ad0",
    "metadata": {
     "editable": true
    },
@@ -1546,7 +1546,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "483a76aa",
+   "id": "3cf12272",
    "metadata": {
     "editable": true
    },
@@ -1556,7 +1556,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b08310e",
+   "id": "737cb12f",
    "metadata": {
     "editable": true
    },
@@ -1568,7 +1568,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0bda2b6a",
+   "id": "a8a9261e",
    "metadata": {
     "editable": true
    },
@@ -1578,7 +1578,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63783fe2",
+   "id": "6be0af92",
    "metadata": {
     "editable": true
    },
@@ -1590,7 +1590,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f22cf2f",
+   "id": "b10091d1",
    "metadata": {
     "editable": true
    },
@@ -1601,7 +1601,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a8e753e",
+   "id": "48674e40",
    "metadata": {
     "editable": true
    },
@@ -1613,7 +1613,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed2d3e5f",
+   "id": "595a9e6e",
    "metadata": {
     "editable": true
    },
@@ -1625,7 +1625,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e39dc22c",
+   "id": "73a0a456",
    "metadata": {
     "editable": true
    },
@@ -1635,7 +1635,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0db9d4f7",
+   "id": "9fd7cbc2",
    "metadata": {
     "editable": true
    },
@@ -1647,7 +1647,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8470958",
+   "id": "4d677ba1",
    "metadata": {
     "editable": true
    },
@@ -1673,7 +1673,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "6bfa1ea4",
+   "id": "12fcace6",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1730,7 +1730,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9ef84b6",
+   "id": "eb7f7e57",
    "metadata": {
     "editable": true
    },
@@ -1742,7 +1742,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78f48748",
+   "id": "2c295b10",
    "metadata": {
     "editable": true
    },
@@ -1754,7 +1754,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9224d5f1",
+   "id": "a53e1b79",
    "metadata": {
     "editable": true
    },
@@ -1764,7 +1764,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45c4747f",
+   "id": "56f9c15d",
    "metadata": {
     "editable": true
    },
@@ -1776,7 +1776,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c462d5b",
+   "id": "56d1e37b",
    "metadata": {
     "editable": true
    },
@@ -1786,7 +1786,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96bb895c",
+   "id": "eb3fd329",
    "metadata": {
     "editable": true
    },
@@ -1798,7 +1798,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb40eb20",
+   "id": "cc9b0325",
    "metadata": {
     "editable": true
    },
@@ -1809,7 +1809,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5bbf9e79",
+   "id": "0fa30cf7",
    "metadata": {
     "editable": true
    },
@@ -1821,7 +1821,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18b6af3c",
+   "id": "1c08d16a",
    "metadata": {
     "editable": true
    },
@@ -1831,7 +1831,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "274baacb",
+   "id": "0d44a15d",
    "metadata": {
     "editable": true
    },
@@ -1843,7 +1843,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff3ddfa9",
+   "id": "e88f8ecc",
    "metadata": {
     "editable": true
    },
@@ -1861,7 +1861,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a21e578c",
+   "id": "0cc660d3",
    "metadata": {
     "editable": true
    },
@@ -1872,7 +1872,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4283508a",
+   "id": "03693b47",
    "metadata": {
     "editable": true
    },
@@ -1884,7 +1884,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1946c46d",
+   "id": "20a53872",
    "metadata": {
     "editable": true
    },
@@ -1894,7 +1894,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d6d2bc13",
+   "id": "2c3ec861",
    "metadata": {
     "editable": true
    },
@@ -1911,7 +1911,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fcd488a4",
+   "id": "77f44e41",
    "metadata": {
     "editable": true
    },
@@ -1925,7 +1925,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95fcc462",
+   "id": "09a4a456",
    "metadata": {
     "editable": true
    },
@@ -1940,7 +1940,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45b6daac",
+   "id": "10e3a3a4",
    "metadata": {
     "editable": true
    },
@@ -1952,7 +1952,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "671e1044",
+   "id": "8b26bbd9",
    "metadata": {
     "editable": true
    },
@@ -1980,7 +1980,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c5d3311",
+   "id": "513fbb54",
    "metadata": {
     "editable": true
    },
@@ -1992,7 +1992,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "184e3a18",
+   "id": "425eeff8",
    "metadata": {
     "editable": true
    },
@@ -2007,7 +2007,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0011f89f",
+   "id": "4b6914a8",
    "metadata": {
     "editable": true
    },
@@ -2018,7 +2018,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "6fd92235",
+   "id": "658ca970",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2217,7 +2217,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ccb7c9a3",
+   "id": "46f4bb9e",
    "metadata": {
     "editable": true
    },
@@ -2229,7 +2229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "052eca82",
+   "id": "e674c7c8",
    "metadata": {
     "editable": true
    },
@@ -2244,7 +2244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3be97359",
+   "id": "6e3bbda9",
    "metadata": {
     "editable": true
    },
@@ -2261,7 +2261,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56a6b1f5",
+   "id": "8e9ac2ba",
    "metadata": {
     "editable": true
    },
@@ -2282,7 +2282,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "e371dcaf",
+   "id": "9884fd7b",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2295,7 +2295,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b6db1c5",
+   "id": "cdeac3ee",
    "metadata": {
     "editable": true
    },
@@ -2305,7 +2305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1074ba6e",
+   "id": "293f0f51",
    "metadata": {
     "editable": true
    },
@@ -2317,7 +2317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "812cc05d",
+   "id": "327a71d8",
    "metadata": {
     "editable": true
    },
@@ -2332,7 +2332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eab0270e",
+   "id": "9b184143",
    "metadata": {
     "editable": true
    },
@@ -2342,7 +2342,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3883c011",
+   "id": "facb4f1d",
    "metadata": {
     "editable": true
    },
@@ -2361,7 +2361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "476392f8",
+   "id": "91730838",
    "metadata": {
     "editable": true
    },
@@ -2371,7 +2371,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e7fcbc9",
+   "id": "e7e3f557",
    "metadata": {
     "editable": true
    },
@@ -2383,7 +2383,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0bf10947",
+   "id": "f8d0f139",
    "metadata": {
     "editable": true
    },
@@ -2393,7 +2393,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a1089ab",
+   "id": "16172933",
    "metadata": {
     "editable": true
    },
@@ -2405,7 +2405,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c29cb3a3",
+   "id": "cf467dc4",
    "metadata": {
     "editable": true
    },
@@ -2415,7 +2415,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba173ff1",
+   "id": "70381e15",
    "metadata": {
     "editable": true
    },
@@ -2427,7 +2427,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7511a6e4",
+   "id": "3200aa57",
    "metadata": {
     "editable": true
    },
@@ -2438,7 +2438,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20dd32f8",
+   "id": "df6ebba2",
    "metadata": {
     "editable": true
    },
@@ -2450,7 +2450,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a266e040",
+   "id": "0cec9eaf",
    "metadata": {
     "editable": true
    },
@@ -2462,7 +2462,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "b5283e4d",
+   "id": "ea9e14cd",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2473,20 +2473,20 @@
     "import numpy\n",
     "from cvxopt import matrix\n",
     "from cvxopt import solvers\n",
-    "P = matrix(numpy.diag([1,0]), tc=’d’)\n",
-    "q = matrix(numpy.array([3,4]), tc=’d’)\n",
-    "G = matrix(numpy.array([[-1,0],[0,-1],[-1,-3],[2,5],[3,4]]), tc=’d’)\n",
-    "h = matrix(numpy.array([0,0,-15,100,80]), tc=’d’)\n",
+    "P = matrix(numpy.diag([1,0]), tc='d')\n",
+    "q = matrix(numpy.array([3,4]), tc='d')\n",
+    "G = matrix(numpy.array([[-1,0],[0,-1],[-1,-3],[2,5],[3,4]]), tc='d')\n",
+    "h = matrix(numpy.array([0,0,-15,100,80]), tc='d')\n",
     "# Construct the QP, invoke solver\n",
     "sol = solvers.qp(P,q,G,h)\n",
     "# Extract optimal value and solution\n",
-    "sol[’x’] \n",
-    "sol[’primal objective’]"
+    "sol['x'] \n",
+    "sol['primal objective']"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e16846f4",
+   "id": "0a62eeaf",
    "metadata": {
     "editable": true
    },
@@ -2498,7 +2498,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef1a4dbe",
+   "id": "dffd3cdc",
    "metadata": {
     "editable": true
    },
@@ -2515,16 +2515,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "251b3924",
+   "id": "9970fc85",
    "metadata": {
     "editable": true
    },
    "source": [
     "subject to $\\boldsymbol{y}^T\\boldsymbol{\\lambda}=0$. Here we defined the vectors $\\boldsymbol{\\lambda} =[\\lambda_1,\\lambda_2,\\dots,\\lambda_n]$ and \n",
     "$\\boldsymbol{y}=[y_1,y_2,\\dots,y_n]$. \n",
-    "With  the slack constants this leads to the additional constraint $0\\leq \\lambda_i \\leq C$.\n",
-    "\n",
-    "**code will be added**"
+    "With  the slack constants this leads to the additional constraint $0\\leq \\lambda_i \\leq C$."
    ]
   }
  ],
diff --git a/doc/src/week46/week46.do.txt b/doc/src/week46/week46.do.txt
index a8d294653..4b718bd2d 100644
--- a/doc/src/week46/week46.do.txt
+++ b/doc/src/week46/week46.do.txt
@@ -1158,15 +1158,15 @@ The following code solves the equations for us
 import numpy
 from cvxopt import matrix
 from cvxopt import solvers
-P = matrix(numpy.diag([1,0]), tc=’d’)
-q = matrix(numpy.array([3,4]), tc=’d’)
-G = matrix(numpy.array([[-1,0],[0,-1],[-1,-3],[2,5],[3,4]]), tc=’d’)
-h = matrix(numpy.array([0,0,-15,100,80]), tc=’d’)
+P = matrix(numpy.diag([1,0]), tc='d')
+q = matrix(numpy.array([3,4]), tc='d')
+G = matrix(numpy.array([[-1,0],[0,-1],[-1,-3],[2,5],[3,4]]), tc='d')
+h = matrix(numpy.array([0,0,-15,100,80]), tc='d')
 # Construct the QP, invoke solver
 sol = solvers.qp(P,q,G,h)
 # Extract optimal value and solution
-sol[’x’] 
-sol[’primal objective’]
+sol['x'] 
+sol['primal objective']
 !ec
 
 !split
@@ -1187,7 +1187,6 @@ subject to $\bm{y}^T\bm{\lambda}=0$. Here we defined the vectors $\bm{\lambda} =
 $\bm{y}=[y_1,y_2,\dots,y_n]$. 
 With  the slack constants this leads to the additional constraint $0\leq \lambda_i \leq C$.
 
-_code will be added_