Hi dear all,
I am using Python2.4.2+NumPy1.0.1 to deal with a parameter
estimation problem with the least square methods. During the
calculations, I use NumPy package to deal with matrix operations,
mostly matrix inversion and trasposition. The dimentions of the
matrices used are about 29x19,19x19 and 29x29.
During the calculation, I noticed an apparent error of
inverion of a 19x19 matrix. Denote this matrix as KK, U=KK^ -1, I
found the product of U and KK is not equivalent to unit matrix! This
apparently violate the definition of inversion. The inversion is
through the function linalg.inv().
I have checked that det(KK)=-1.2E+40. At first, I thought the
error may be caused by such a large determinant, so I scaled it as
LL=KK/100, then invert LL. Since det(LL)=11.5 and all its elements are
within -180.0 to 850.0, this seems easier. But the result is still not
correct, the product of LL^-1 thus obtained and LL still not unit
matrix ... At the same time, the inversion results of some 29x19
matrices are correct.
So, can you tell me what goes wrong? Is this a bug in
Numpy.linalg? How to deal with this situation? If you need, I can
post the matrix I used below, but it is so long,so not at the moment.
Thanks in advance!
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