15
votes

I have a matrix K of dimensions n x n. I want to create a new block diagonal matrix M of dimensions N x N, such that it contains d blocks of matrix K as its diagonal.

I would have directly used M = blkdiag(K,K,K) etc. had d been smaller. Unfortunately, d is very large and I don't want to manually write the formula with d exactly same arguments for the blkdiag() function.

Is there any shorter, smarter way to do this?

5

5 Answers

24
votes

you can use kron for that.

M = kron(X,Y)

returns the Kronecker tensor product of X and Y. The result is a large array formed by taking all possible products between the elements of X and those of Y. If X is m-by-n and Y is p-by-q, then kron(X,Y) is m*p-by-n*q. So in your case something like this will do:

M = kron(eye(L),K)

with L the # of blocks.

3
votes
tmp = repmat({K},d,1);
M = blkdiag(tmp{:});

You should never use eval, or go into for loops unnecessarily. Kron is a very elegant way. Just wanted to share this as it also works.

2
votes

The following should work:

d=5; K=eye(3); T = cell(1,d);

for j=1:d T{j} =K; end

M = blkdiag(T{:})

0
votes
s = 'A,';
s = repmat(s,[1,n2]);
s = ['B=blkdiag(', s(1:end-1),');'];
eval(s);

It can be faster than using kron-eye.

0
votes

A "for" loop may might help. Like:

M = k;
for i=1:N/n - 1
    M=blkdiag(M,k);
end