[SciPy-User] qr decompostion gives negative q, r ?

[Virgil Stokes]

On 2012-11-21 00:11, Charles R Harris wrote:
>
>
> On Tue, Nov 20, 2012 at 3:59 PM, Virgil Stokes <vs at it.uu.se 
> <mailto:vs at it.uu.se>> wrote:
>
>     On 2012-11-20 23:43, Charles R Harris wrote:
>>
>>
>>     On Tue, Nov 20, 2012 at 3:03 PM, Virgil Stokes <vs at it.uu.se
>>     <mailto:vs at it.uu.se>> wrote:
>>
>>         On 2012-11-20 22:33, Da?id wrote:
>>         > The QR descomposition is finding two matrices with certain
>>         properties such that:
>>         >
>>         > A = Q·R
>>         >
>>         > But, if both Q and R are multiplied by -1, (-Q)·(-R) = Q·R
>>         = A, still
>>         > the same matrix. If Q is orthogonal, -Q is also. The sign is,
>>         > therefore, arbitrary.
>>         >
>>         > On Tue, Nov 20, 2012 at 12:01 AM, Virgil Stokes
>>         <vs at it.uu.se <mailto:vs at it.uu.se>> wrote:
>>         >> I am using the latest versions of numpy (from
>>         >> numpy-1.7.0b2-win32-superpack-python2.7.exe) and scipy (from
>>         >> scipy-0.11.0-win32-superpack-python2.7.exe ) on a windows
>>         7 (32-bit)
>>         >> platform.
>>         >>
>>         >> I have used
>>         >>
>>         >> import numpy as np
>>         >> q,r = np.linalg.qr(A)
>>         >>
>>         >> and compared the results to what I get from MATLAB (R2010B)
>>         >>
>>         >> [q,r] = qr(A)
>>         >>
>>         >> The q,r returned from numpy are both the negative of the
>>         q,r returned
>>         >> from MATLAB for the same matrix A. I believe that theq,r
>>         returned from
>>         >> MATLAB are correct. Why am I getting their negative from
>>         numpy?
>>         >>
>>         >> Note, I have tried this on several different matrices ---
>>          numpy always
>>         >> gives the negative of MATLAB's.
>>         >>
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>>         Thanks David,
>>         I am well aware of this; but, I am using the QR decomposition
>>         for a
>>         convariance (PD matrix) and the negative R is not very useful
>>         in this
>>         case and the numpy result, IMHO should not be the default.
>>
>>
>>     What is your application?
>     My application is the propagation of the factorized R matrix in
>     the Kalman filter, where the QR factorization is for the
>     covariance matrix in the KF recursions.
>
>
> That is what I suspected. However, the factorized matrices are usually 
> U^t*D*U or U^t * U, so I think you are doing something wrong.
No Chuck,
You are referring to Bierman's factorization which is just one of the 
factorizations possible. I am using a standard and well-documented form 
of the so-called "square-root" Kalman filters (just Google on this and 
be enlightened). Again, there many papers/books that discuss the QR 
factorization implementation for both the Kalman filter and Kalman smoother.
>
> Chuck
>
>
>
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