@@ -11,4 +11,4 @@ So far, we were used the simple [`numpy.linalg.inv()`](https://numpy.org/doc/sta
...
@@ -11,4 +11,4 @@ So far, we were used the simple [`numpy.linalg.inv()`](https://numpy.org/doc/sta
In the above figures, the quantity 'inv' refers to the `numpy.linalg.inv()` function. The quantity 'k' refers to the index of the off-diagonal array; k>0 for diagonals above the main diagonal, and k<0 for diagonals below the main diagonal.
In the above figures, the quantity 'inv' refers to the `numpy.linalg.inv()` function. The quantity 'k' refers to the index of the off-diagonal array; k>0 for diagonals above the main diagonal, and k<0 for diagonals below the main diagonal.
To resolve this issue, we propose to use the Moore-Penrose pseudo-inverse method.
To resolve this issue, we propose to use the Moore-Penrose pseudo-inverse method. This method calculates the generalized inverse of a matrix using its singular-value decomposition (SVD) and including all large singular values. We use the numpy inbuild function [`numpy.linalg.pinv()`](https://numpy.org/doc/stable/reference/generated/numpy.linalg.pinv.html).
