small adjustment

doc/pub/week46/html/._week46-bs027.html

@@ -322,6 +322,7 @@ sol <span style="color: #666666">=</span> solvers<span style="color: #666666">.<
 <span style="color: #408080; font-style: italic"># Extract optimal value and solution</span>
 sol[<span style="color: #BA2121">&#39;x&#39;</span>] 
 sol[<span style="color: #BA2121">&#39;primal objective&#39;</span>]
+<span style="color: #008000">print</span>(sol[<span style="color: #BA2121">&#39;x&#39;</span>] )
 </pre>
 </div>
       </div>

doc/pub/week46/html/._week46-bs028.html

@@ -272,7 +272,7 @@ $$
 With  the slack constants this leads to the additional constraint \( 0\leq \lambda_i \leq C \).
 </p>
 
-<p>Using the <b>CVXOPT</b> library, the matrix \( P \) would then be defined by the </p>
+<p>Using the <b>CVXOPT</b> library, the matrix \( P \) would then be defined by the above matrix while the KKT conditions would all be collected by the matrix \( G \).</p>
 
 <p>
 <!-- navigation buttons at the bottom of the page -->

doc/pub/week46/html/week46-reveal.html

@@ -1566,6 +1566,7 @@ sol = solvers.qp(P,q,G,h)
 <span style="color: #228B22"># Extract optimal value and solution</span>
 sol[<span style="color: #CD5555">&#39;x&#39;</span>] 
 sol[<span style="color: #CD5555">&#39;primal objective&#39;</span>]
+<span style="color: #658b00">print</span>(sol[<span style="color: #CD5555">&#39;x&#39;</span>] )
 </pre>
 </div>
       </div>
@@ -1602,7 +1603,7 @@ $$
 With  the slack constants this leads to the additional constraint \( 0\leq \lambda_i \leq C \).
 </p>
 
-<p>Using the <b>CVXOPT</b> library, the matrix \( P \) would then be defined by the </p>
+<p>Using the <b>CVXOPT</b> library, the matrix \( P \) would then be defined by the above matrix while the KKT conditions would all be collected by the matrix \( G \).</p>
 </section>
 
 <section>

doc/pub/week46/html/week46-solarized.html

@@ -1432,6 +1432,7 @@ sol = solvers.qp(P,q,G,h)
 <span style="color: #228B22"># Extract optimal value and solution</span>
 sol[<span style="color: #CD5555">&#39;x&#39;</span>] 
 sol[<span style="color: #CD5555">&#39;primal objective&#39;</span>]
+<span style="color: #658b00">print</span>(sol[<span style="color: #CD5555">&#39;x&#39;</span>] )
 </pre>
 </div>
       </div>
@@ -1466,7 +1467,7 @@ $$
 With  the slack constants this leads to the additional constraint \( 0\leq \lambda_i \leq C \).
 </p>
 
-<p>Using the <b>CVXOPT</b> library, the matrix \( P \) would then be defined by the </p>
+<p>Using the <b>CVXOPT</b> library, the matrix \( P \) would then be defined by the above matrix while the KKT conditions would all be collected by the matrix \( G \).</p>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="basic-ideas-of-the-principal-component-analysis-pca">Basic ideas of the Principal Component Analysis (PCA) </h2>

doc/pub/week46/html/week46.html

@@ -1509,6 +1509,7 @@ sol <span style="color: #666666">=</span> solvers<span style="color: #666666">.<
 <span style="color: #408080; font-style: italic"># Extract optimal value and solution</span>
 sol[<span style="color: #BA2121">&#39;x&#39;</span>] 
 sol[<span style="color: #BA2121">&#39;primal objective&#39;</span>]
+<span style="color: #008000">print</span>(sol[<span style="color: #BA2121">&#39;x&#39;</span>] )
 </pre>
 </div>
       </div>
@@ -1543,7 +1544,7 @@ $$
 With  the slack constants this leads to the additional constraint \( 0\leq \lambda_i \leq C \).
 </p>
 
-<p>Using the <b>CVXOPT</b> library, the matrix \( P \) would then be defined by the </p>
+<p>Using the <b>CVXOPT</b> library, the matrix \( P \) would then be defined by the above matrix while the KKT conditions would all be collected by the matrix \( G \).</p>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="basic-ideas-of-the-principal-component-analysis-pca">Basic ideas of the Principal Component Analysis (PCA) </h2>

doc/src/week46/week46.do.txt

@@ -1140,6 +1140,7 @@ sol = solvers.qp(P,q,G,h)
 # Extract optimal value and solution
 sol['x'] 
 sol['primal objective']
+print(sol['x'] )
 !ec
 
 !split
@@ -1160,7 +1161,7 @@ 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 $0\leq \lambda_i \leq C$.
 
-Using the _CVXOPT_ library, the matrix $P$ would then be defined by the 
+Using the _CVXOPT_ library, the matrix $P$ would then be defined by the above matrix while the KKT conditions would all be collected by the matrix $G$.