Hello,
a technique that allows something similar to what you are suggesting
is to use polyphase filters. The difference is that you will not
process contiguous vectors, but (for a 2-phase decomposition example)
process the even samples with one stage of the filter and the odd
samples with another

[Apologies for cross-postings]
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Hi,
thanks for the answer so far.
A polyphase filter is a nice idea but it does not answer the problem.
The signal has to be demultiplexed (decimated), the different streams
have to be filtered, the results must be added to get the final output
signal.
My question has a different target.
Imagine

Find the roots, pair the complex conjugate roots and distribute the
pairs and single real roots evenly (how exactly?) in the two filters.
Matlab at least has facilities finding roots of large polynomials.
-olli
On Wed, Jan 19, 2011 at 4:56 PM, Uli Brueggemann
uli.brueggem...@gmail.com wrote:

Hi Uli
I don't know if this will be useful for your situation, but a simple method for
decomposing your kernel is to simply chop it in two. So for a kernel:
1 2 3 4 5 6 7 8
You can decompose it into two zero padded kernels:
1 2 3 4 0 0 0 0
0 0 0 0 5 6 7 8
And sum the results of convolving

Thomas,
I suppose that a decomposition of a n-taps kernel into n zero-padded
kernels would directly lead to the basics of the convolution algorithm
:-)
But your proposal also introduces a parallel computation, where the
results have to be offset and added (incl. overlap treatment).
My question is

I see, most people want *more* parallelisation in their algorithms, not less ;)
Perhaps you could make a 'guess' at the first filter and then solve the problem
of finding a second filter which gives you the desired result. Since:
f1*f2=result
...we know f1 f2 and so can find 'result', and...