Intel® oneAPI Math Kernel Library
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block tridiagonal and block upper hessenberg eigenvalue solvers


Dear all,

I am looking at some options in order to compare the performance of eigenvalue solvers for

+ symmetric block tridiagonal 

+ block upper hessenberg matrices.

If I iterate in a single vector fashion(not in blocks), I can use stevd and hseqr, respectively(I guess), since the manual and selection tree points to these routines.

But if I convert to block iteration mode, is there a direct replacement for these routines when the matrices become block symmetric tridiagonal or block upper hessenberg.

What would be the most efficient way for the computation of the eigenvalues and eigenvectors in the case of block iterations for a symmetric and hessenberg matrix?



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