[SciPy-User] fast small matrix multiplication with cython?
Skipper Seabold
jsseabold@gmail....
Tue Dec 7 11:39:28 CST 2010
On Tue, Dec 7, 2010 at 12:17 PM, Charles R Harris
<charlesr.harris@gmail.com> wrote:
>
>
> On Tue, Dec 7, 2010 at 10:05 AM, <josef.pktd@gmail.com> wrote:
<snip>
>> It's still a linear filter, non-linear optimization comes in because
>> the exact loglikelihood function for ARMA is non-linear in the
>> coefficients.
>> (There might be a way to calculate the derivative in the same loop,
>> but that's a different issue.)
>>
>
> The unscented Kalman filter is a better way to estimate the covariance of a
> non-linear process, think of it as a better integrator. If the propagation
> is easy to compute, which seems to be the case here, it will probably save
> you some time. You might even be able to use the basic idea and skip the
> Kalman part altogether.
>
> My general aim here is to optimize the algorithm first before getting caught
> up in the details of matrix multiplication in c. Premature optimization and
> all that.
>
Hmm I haven't seen this mentioned much in what I've been reading or
the documentation on existing software for ARMA processes, so I never
thought much about it. I will have a closer look. Well, google turns
up this thread...
There is another optimization that I could employ by switching to fast
recursions when the state variance converges to its steady state, but
this makes it less general for future enhancements (ie., time varying
coefficients). Maybe I will go ahead and try it.
Skipper
More information about the SciPy-User
mailing list