passionfly
Oct30-10, 03:33 AM
Dear all:
For a standard linear system, y(n*1)=A(n*n)x(n*1)
If y is exact and A is well-conditioned, it is easy to calculate x.
However, if y has some disturbances or some errors, at the same time, A matrix is ill-conditioned. It is difficult to accurately obtain vector x. Alternatively, we can measure more components of y (becomes m*1, where m>n) and use the least square method to calculate the approximate x vector.
It is a common problem so I guess there should be some ready-to-use Fortran codes. Anybody knows where can I find this algorithms or codes? Any advice or suggestion is welcomed!
Thank you very much and with best wishes,
Thomas
For a standard linear system, y(n*1)=A(n*n)x(n*1)
If y is exact and A is well-conditioned, it is easy to calculate x.
However, if y has some disturbances or some errors, at the same time, A matrix is ill-conditioned. It is difficult to accurately obtain vector x. Alternatively, we can measure more components of y (becomes m*1, where m>n) and use the least square method to calculate the approximate x vector.
It is a common problem so I guess there should be some ready-to-use Fortran codes. Anybody knows where can I find this algorithms or codes? Any advice or suggestion is welcomed!
Thank you very much and with best wishes,
Thomas