[SciPy-User] qr decompostion gives negative q, r ?
Charles R Harris
charlesr.harris@gmail....
Tue Nov 20 17:55:16 CST 2012
On Tue, Nov 20, 2012 at 4:26 PM, Virgil Stokes <vs@it.uu.se> wrote:
> On 2012-11-21 00:11, Charles R Harris wrote:
>
>
>
> On Tue, Nov 20, 2012 at 3:59 PM, Virgil Stokes <vs@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@it.uu.se> wrote:
>>
>>> On 2012-11-20 22:33, Daπid 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@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.
>>> >>
>>> >> _______________________________________________
>>> >> SciPy-User mailing list
>>> >> SciPy-User@scipy.org
>>> >> http://mail.scipy.org/mailman/listinfo/scipy-user
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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.
>
Yes I am familiar with square root Kalman filters, I've even written a few.
Chuck
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