[SciPy-User] qr decompostion gives negative q, r ?
Tue Nov 20 18:56:13 CST 2012
On 2012-11-21 01:48, Alejandro Weinstein wrote:
> On Tue, Nov 20, 2012 at 5:36 PM, Virgil Stokes <firstname.lastname@example.org> wrote:
>> Using np.linalg.qr(A) I get the following for R (3x3) which is
>> "square-root" of the covariance matrix:
>> array([[ -1.00124922e+03, 4.99289918e+00, 0.00000000e+00],
>> [ 0.00000000e+00, -1.00033071e+02, 5.62045938e-04],
>> [ 0.00000000e+00, 0.00000000e+00, -9.98419272e-03]])
>> which is clearly not PD, since the it's 3 eigenvalues (diagonal
>> elements) are all negative.
> But why you expect R to be PD?
Because R*R^T = P (a covariance matrix). One important reason for
using the QR factorization in the KF is to ensure that R is always PD
during the recursions.
> The QR decomposition  is
> A = QR with Q^T Q = I and R upper diagonal.
>  http://en.wikipedia.org/wiki/QR_factorization
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