Dear List: I am having to build a block-diagonal matrix (vl) and am currently using the following code. I<-diag(sample.size) vl<-kronecker(I,vl.mat) This code works fine, but for large N, it is a huge memory hog. Is there a more efficient method for constructing vl? Thanks, Harold [[alternative HTML version deleted]]
On Mon, 3 Jan 2005, Doran, Harold wrote:> Dear List: > > I am having to build a block-diagonal matrix (vl) and am currently using > the following code. > > I<-diag(sample.size) > vl<-kronecker(I,vl.mat) > > > This code works fine, but for large N, it is a huge memory hog. Is there > a more efficient method for constructing vl? >Obvious alternatives such as nr<-nrow(v1.mat) nc<-ncol(v1.mat) result<-matrix(0,nrow=sample.size*nr,ncol=sample.size*nc) for(i in 1:sample.size){ result[ (i-1)*nr+1:nr, (i-1)*nc+1:nc]<-v1.mat } or nr<-nrow(v1.mat) nc<-ncol(v1.mat) iy<-as.vector(outer(rep(1:nc,each=nr),(0:(sample.size-1))*nc,"+")) ix<-as.vector(outer(rep(1:nr, nc),(0:(sample.size-1))*nr,"+")) result<-matrix(0,nrow=sample.size*nr,ncol=sample.size*nc) result[cbind(ix,iy)]<-v1.mat seem to take a little less memory and about the same time, but constructing a large block-diagonal matrix is intrinsically an inefficient thing to do. -thomas
Thomas Lumley <tlumley <at> u.washington.edu> writes:
:
: On Mon, 3 Jan 2005, Doran, Harold wrote:
:
: > Dear List:
: >
: > I am having to build a block-diagonal matrix (vl) and am currently using
: > the following code.
: >
: > I<-diag(sample.size)
: > vl<-kronecker(I,vl.mat)
: >
: >
: > This code works fine, but for large N, it is a huge memory hog. Is there
: > a more efficient method for constructing vl?
: >
:
: Obvious alternatives such as
:
: nr<-nrow(v1.mat)
: nc<-ncol(v1.mat)
: result<-matrix(0,nrow=sample.size*nr,ncol=sample.size*nc)
: for(i in 1:sample.size){
: result[ (i-1)*nr+1:nr, (i-1)*nc+1:nc]<-v1.mat
: }
:
: or
: nr<-nrow(v1.mat)
: nc<-ncol(v1.mat)
:
iy<-as.vector(outer(rep(1:nc,each=nr),(0:(sample.size-1))*nc,"+"))
: ix<-as.vector(outer(rep(1:nr, nc),(0:(sample.size-1))*nr,"+"))
: result<-matrix(0,nrow=sample.size*nr,ncol=sample.size*nc)
: result[cbind(ix,iy)]<-v1.mat
:
: seem to take a little less memory and about the same time,
: but constructing a large block-diagonal matrix is intrinsically an
: inefficient thing to do.
You could try combining some of those ideas with use of the SparseM
package.