Fast matrix computation of a 'riccati like' equation in a 'for' loop
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hello, i'd like to optimize the following computation :
for k=1:N
P = A*(P + Q)*A' + W
end
with N very large (>1e6) ; A,P,Q and W are [50x50] real matrices.
I found that the following code was slighty better (few %):
for k=1:N
Ptmp = P+Q;
Ptmp = A*tmp;
Ptmp = Ptm*A';
P = Ptmp + W;
end
I tried the 'mtimesx' function, but it was slower than the previous code (I used all the optiosn : matlab,speed, speedomp).
Is there a better way to do this computation? Thank you
更多回答(2 个)
Matz Johansson Bergström
2014-8-20
I would like to preface my answer by saying that your code is essentially as fast as it gets. Matlab is very fast when it comes to Matrix-Matrix mutliplication. It was designed to. Without knowing the structure of the matrices I can only assume that we can apply ordinary matrix laws to break them up.
In the first expression we have two multiplications and two additions to do in a loop. On my computer, your first code takes 37.492 s. The second code takes 37.412 s. If we avoid to recompute multiplications and transposes, we can get it down to 35 s by just breaking up the expression and moving the computation around. This is only a 7 percent speedup and I think that this is as good as it gets.
We have
P = A*(P + Q)*A' + W
P = (A*P + A*Q)*A' + W
P = A*P*A' + A*Q*A' + W
This is four multiplications and two additions, but since we know A*Q*A'+W is never changing, we store it before the iterations. The resulting expression requires two multiplications and one addition. So, we only saved one addition, but the time spent is accumulated for each iteration.
I also store A', which provides some slight speedup. I also avoid using Ptmp, because we don't need to copy the data for each iteration, we can use P directly.
The code
%Preprocessing:
At = A';
con = A*Q*At + W;
for k = 1:N
P = A*P;
P = P*At;
P = P + con;
end
I believe it is correct. I run it on 100 iterations becase by running it for 1e6 iterations P consists of Inf elements.
So, I only managed to remove one addition, but I believe it is a good as it gets. Hopefully someone else can find a better way to do it.
2 个评论
Matt J
2014-8-20
编辑:Matt J
2014-8-20
If A is symmetric and everything is k-independent, you can do as follows. I find it to be 15 times faster than the for loop for N=1e6 and 50x50 input matrices.
Z=A*Q*At+W;
An=A^N;
[R,E]=eig(A);
e=bsxfun(@power,diag(E),0:N-1);
Rk=kron(R,R); Ek=e*e.';
Z=bsxfun(@times,Rk, Ek(:).')*(Z(:).'*Rk).';
Z=reshape(Z,size(A));
P=An*P0*An.' + Z;
2 个评论
Matt J
2014-8-20
Well, never mind. It also assumes A is k-independent. I don't think there's much that can be done to accelerate your code unless there is simplifying structure in A, Q and W and their k-dependence. If there is, it would be advisable to update your post with that information. Are Q and W also k-dependent?
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