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