small update

2023-09-03 14:22:23 +02:00
parent 3cc1f701c0
commit 7f3ecb7bb7
103 changed files with 16669 additions and 1168 deletions
@@ -147,9 +147,11 @@ values and the column vectors of $\bm{V}$.
 =====  Code for SVD and Inversion of Matrices =====

 How do we use the SVD to invert a matrix $\bm{X}^\bm{X}$ which is singular or near singular?
-The simple answer is to use the linear algebra function for pseudoinvers, that is
+The simple answer is to use the linear algebra function for the computation of the pseudoinverse of a given matrix $\bm{X}$, that is
 !bc pycod
-Ainv = np.linlag.pinv(A)
+import numpy as np
+X = np.array( [ [1,2,3],[2,4,5],[3,5,6]])
+Xinv = np.linlag.pinv(X)
 !ec

 Let us first look at a matrix which does not causes problems and write our own function where we just use the SVD.