- #1
divB
- 87
- 0
Hi,
From a paper I got a matrix like this which I use to solve the over-determined system [itex]\mathbf{z} = \mathbf{U} \mathbf{a}[/itex]:
[tex]
\mathbf{U} = \left[\begin{matrix}
x(3) & x(3)^2 & x(3)^3 & x(2) & x(2)^2 & x(2)^3 & x(1) & x(1)^2 & x(1)^3\\
x(4) & x(4)^2 & x(4)^3 & x(3) & x(3)^2 & x(3)^3 & x(2) & x(2)^2 & x(2)^3\\
x(5) & x(5)^2 & x(5)^3 & x(4) & x(4)^2 & x(4)^3 & x(3) & x(3)^2 & x(3)^3\\
x(6) & x(6)^2 & x(6)^3 & x(5) & x(5)^2 & x(5)^3 & x(4) & x(4)^2 & x(4)^3\\
\end{matrix}\right]
[/tex]
However, in my experiments I found that this matrix is very unstable and has high condition numbers. No matter which "signal" x I plug into, even if the x are drawn from a Gaussian distribution.
Can anyone tell me why this matrix is so unstable or if I am doing something wrong?
The crazy thing is that the paper suggests the algorithm works without any problems (using the same parameter set) but I just can't reproduce this ...
Thanks,
div
From a paper I got a matrix like this which I use to solve the over-determined system [itex]\mathbf{z} = \mathbf{U} \mathbf{a}[/itex]:
[tex]
\mathbf{U} = \left[\begin{matrix}
x(3) & x(3)^2 & x(3)^3 & x(2) & x(2)^2 & x(2)^3 & x(1) & x(1)^2 & x(1)^3\\
x(4) & x(4)^2 & x(4)^3 & x(3) & x(3)^2 & x(3)^3 & x(2) & x(2)^2 & x(2)^3\\
x(5) & x(5)^2 & x(5)^3 & x(4) & x(4)^2 & x(4)^3 & x(3) & x(3)^2 & x(3)^3\\
x(6) & x(6)^2 & x(6)^3 & x(5) & x(5)^2 & x(5)^3 & x(4) & x(4)^2 & x(4)^3\\
\end{matrix}\right]
[/tex]
However, in my experiments I found that this matrix is very unstable and has high condition numbers. No matter which "signal" x I plug into, even if the x are drawn from a Gaussian distribution.
Can anyone tell me why this matrix is so unstable or if I am doing something wrong?
The crazy thing is that the paper suggests the algorithm works without any problems (using the same parameter set) but I just can't reproduce this ...
Thanks,
div