Dear colleagues, is it possible to find eigenvalues and eigenvectors by means of MKL routines in the following case: Ax = kx, where A is a sparse, general, complex matrix. I found the routines for sparse symmetric/hermitian matrices but not for general sparse matrices.
Iosif, that's not possible for general sparse cases for now. one of the existing solution is feast 3.0 which is on our radar but without some timeline at this moment. Do you have some specific timeline when you need to have such functionality available?
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Hi Losif,
For general sparse matrix, The Pardiso or DSS solver may be you wanted.
Best Regards,
Ying
Hi Losif,
then other option may be :
The Extended Eigensolver functionality offers:
• Real/Complex and Single/Double precisions: double precision is recommended to provide better accuracy
of eigenpairs.
• Reverse communication interfaces (RCI) provide maximum flexibility for specific applications. RCI are
independent of matrix format and inner system solvers, so you must provide your own linear system
solvers (direct or iterative) and matrix-matrix multiply routines.
• Predefined driver interfaces for dense, LAPACK banded, and sparse (CSR) formats are less flexible but are
optimized and easy to use:
• The Extended Eigensolver interfaces for dense matrices are likely to be slower than the comparable
LAPACK routines because the FEAST algorithm has a higher computational cost.
7 Intel® Math Kernel Library Reference Manual
1628
• The Extended Eigensolver interfaces for banded matrices support banded LAPACK-type storage.
• The Extended Eigensolver sparse interfaces support compressed sparse row format and use the Intel
MKL PARDISO solver.
Best wishes,
Ying
Iosif, that's not possible for general sparse cases for now. one of the existing solution is feast 3.0 which is on our radar but without some timeline at this moment. Do you have some specific timeline when you need to have such functionality available?