Week 12 Mark edits

Author: austinwn

12.5.1 The Inverse Power Method.ipynb

@@ -122,7 +122,7 @@
       "    x = x / np.sqrt( np.transpose( x ) * x )\n",
       "    \n",
       "    # Notice we compute the Rayleigh quotient with matrix A, not Ainv.  This is because\n",
-      "    # the eigenvalue of A is an eigenvalue of Ainv\n",
+      "    # the eigenvector of A is an eigenvector of Ainv\n",
       "    \n",
       "    print( 'Rayleigh quotient with vector x:', np.transpose( x ) * A * x / ( np.transpose( x ) * x ))\n",
       "    print( 'inner product of x with v3     :', np.transpose( x ) * V[ :, 3 ] )\n",

12.5.2 Shifting the Inverse Power Method.ipynb

@@ -131,7 +131,7 @@
       "    x = x / np.sqrt( np.transpose( x ) * x )\n",
       "    \n",
       "    # Notice we compute the Rayleigh quotient with matrix A, not Ainv.  This is because\n",
-      "    # the eigenvalue of A is an eigenvalue of Ainv\n",
+      "    # the eigenvector of A is an eigenvector of Ainv\n",
       "    \n",
       "    print( 'Rayleigh quotient with vector x:', np.transpose( x ) * A * x / ( np.transpose( x ) * x ))\n",
       "    print( 'inner product of x with v3     :', np.transpose( x ) * V[ :, 3 ] )\n",

12.5.3 The Rayleigh Quotient Iteration.ipynb

@@ -132,9 +132,9 @@
       "    x = x / np.sqrt( np.transpose( x ) * x )\n",
       "    \n",
       "    # Notice we compute the Rayleigh quotient with matrix A, not Ainv.  This is because\n",
-      "    # the eigenvalue of A is an eigenvalue of Ainv\n",
+      "    # the eigenvector of A is an eigenvector of Ainv\n",
       "    \n",
-      "    mu = np.transpose( x ) * A * x / ( np.transpose( x ) * x )\n",
+      "    mu = np.transpose( x ) * A * x\n",
       "    \n",
       "    # The above returns a 1 x 1 matrix.  Let's set mu to the scalar\n",
       "    \n",