topic Quote:Michael W. wrote: in Intel® oneAPI Math Kernel Library

Eigenvalue decomposition with CSR sparse matrix

Michael_W_1 — Tue, 04 Mar 2014 10:42:22 GMT

Hi,

at the moment I use dsyevd to compute the eigenvalues and eigenvectors of a large matrix A (n = 22000). This takes about half an hour. I know that they are a lot of zeros in matrix A (90% are zeros). Matrix A is stored as CSR sparse matrix.

- Is there a function to compute the eigenvalues and eigenvectors of a CSR sparse matrix?

- Is there a function to convert a CSR sparse matrix to a band matrix? Then I could use dsbevd.

Regards Michael

yes, since the version 11.0

Gennady_F_Intel — Tue, 04 Mar 2014 15:10:18 GMT

yes, since the version 11.0 mkl contains the Extended Eigensolver Routines -- please see reference manual for more details. These routines support CSR format too.

there are no routines convert CSR->Band format, ut there are a number of routines conversion csr<->dense

Quote:Michael W. wrote:

mecej4 — Tue, 04 Mar 2014 15:25:00 GMT

Michael W. wrote:

... I use dsyevd to compute the eigenvalues and eigenvectors of a large matrix A (n = 22000). Matrix A is stored as CSR sparse matrix.

There is something amiss here.

The ?syevd routines operate on a matrix kept in dense-storage form.
You cannot pass a matrix stored in CSR form to such a routine.
A banded matrix is sparse, but not vice versa. If you know that the matrix is banded, look for an eigenvalue/vector routine that is tailored to such matrices, rather than to the more general sparse matrix type.

Quote:Michael W. wrote:A = B

mecej4 — Thu, 06 Mar 2014 19:11:00 GMT

Michael W. wrote:
A = B x B'

That is very useful information. The eigenvalues of A = B.B' are obtainable from the non-zero singular values of B. There are several routines available to compute the SVD (Singular Value Decomposition) of a dense matrix; see, for example, ?gesvd() in Lapack/MKL. You are probably in need of only the singular values and may look for a routine that allows you to specify that the singular vectors are unwanted.

Hi,

Michael_W_1 — Mon, 10 Mar 2014 10:22:00 GMT

Hi,

1. What I have as Input is CSR sparse Matrix B (number of rows: 20000, number of columns 100000)

2. Intermediate result A = B x B'

3. Intermediate result V, D = dsyevd(A) where V are the eigenvectors and D are the eigenvalues

4. Intermediate result E: diagonal Matrix. The elements on the diagonal are the inverse values of D.

5. Final Result W = V x E

So if you know a faster to compute W from B, please let me know.

Regards

Michael